Soil Organic Carbon Dynamics under Short-term ...Soil Organic Carbon Dynamics under Short-term Conservation Agriculture Cropping Systems in Cambodia Lyda Hok North Carolina A&T State

Soil Organic Carbon Dynamics under Short-term Conservation Agriculture

Cropping Systems in Cambodia

Lyda Hok

North Carolina A&T State University

A dissertation submitted to the graduate faculty

in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

Department: Energy and Environmental Systems

Major: Energy and Environmental Systems

Major Professor: Dr. Manuel R. Reyes

Greensboro, North Carolina

2014

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The Graduate School North Carolina Agricultural and Technical State University

This is to certify that the Doctoral Dissertation of

Lyda Hok

has met the dissertation requirements of

North Carolina Agricultural and Technical State University

Greensboro, North Carolina 2014

Approved by:

Dr. Manuel R. Reyes Major Professor

Dr. Gudigopuram B. Reddy Committee Member

Dr. João C.D.M. Sá Co-Major Professor

Dr. Sanjiv Sarin Associate Vice Chancellor of Research and Graduate Dean

Dr. Charles W. Raczkowski Committee Member

Dr. Muchha R. Reddy Committee Member

Dr. Florent E. Tivet Committee Member

Dr. Keith A. Schimmel Department Chair

Dr. Susan S. Andrews Committee Member

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© Copyright by

Lyda Hok

2014

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Biographical Sketch

Lyda Hok was born on November 7, 1985, in Phnom Penh, Cambodia. He earned his

Bachelor of Science degree in Agronomy from Royal University of Agriculture, Cambodia, in

2006. He received Master of Science degree in Agronomy from Khon Kaen University,

Thailand, in 2009. He is the candidate for the Ph.D. in Energy and Environmental Systems.

While pursuing his doctoral degree, he worked as a graduate research assistant to the Department

of Natural Resources and Environmental Design under School of Agriculture and Environmental

Sciences.

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Dedication

To my dearest parents, Saroeun Hok and Pao Chhin, for their love, support and guidance.

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Acknowledgments

I would like to express my deepest gratitude to Dr. Manuel Reyes, my major professor,

and Dr. João Carlos de Moraes Sá, my co-major professor for their excellent guidance, patience,

encouragement and advice during my dissertation research and writing.

I am sincerely grateful to my dissertation committee members, Dr. Charles Raczkowski,

Dr. Gudigopuram Reddy, Dr. Muchha Reddy, Dr. Florent Tivet, and Dr. Susan Andrews, and the

Ph.D. program coordinator Dr. Keith Schimmel for their valuable advice on developing the

research proposal and improving the dissertation.

I would like to thank the Feed the Future-Sustainable Agriculture and Natural Resource

Management Innovation Lab for financial support during my study in the United States, General

Directorate of Agriculture of Cambodia for providing experimental trials, French Agency for

Development, French Global Environment Funds and Conservation Agriculture Network in

South-East Asia for funding this research, and Laboratório de Matéria Orgânica do Solo of State

University of Ponta Grossa for a great support to develop soil analyses for this dissertation.

My words of appreciation also go to Ms. Leonor Lugo and Ms. Jaqueline A. Gonçalves

for their love and support, Brazilian professors and friends for assistance in laboratory analyses,

and Cambodian colleagues for their invaluable support in experimental management and soil

sampling. Their humor and commitment to long hours of work are much appreciated.

I would especially like to express my heartfelt respect for my dearest parents, Saroeun

Hok and Pao Chhin, my brothers Vantha Hok, Vandy Hok and Leanghak Hok, and my fiancée

Sophathya Cheam, who always love, support, care and believe in me, no matter what.

Last but not least, I am grateful for the opportunities and experiences I had at North

Carolina Agricultural and Technical State University. AGGIE PRIDE!

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Table of Contents

List of Figures ................................................................................................................................ xi

List of Tables ............................................................................................................................... xiii

Abstract ............................................................................................................................................1

CHAPTER 1 General Introduction .................................................................................................3

1.1 Research Justification .........................................................................................................3

1.2 Research Objectives ............................................................................................................8

1.3 Research Hypothesis ...........................................................................................................9

CHAPTER 2 Literature Review ...................................................................................................10

2.1 Conservation Agriculture Practices and Adoption ...........................................................10

2.2 Contribution of Tillage-induced Soil Carbon Loss to Global Warming ..........................13

2.3 Soil Carbon Sequestration under Conservation Agriculture ............................................15

2.4 Soil Aggregate Stability and Soil Carbon Sequestration ..................................................21

CHAPTER 3 Short-term Conservation Agriculture Impacts on Total, Particulate

and Mineral-associated Soil Organic Carbon in a Savanna Tropical Agro-

ecosystem .......................................................................................................................................25

Abstract ...................................................................................................................................25

3.1 Introduction .......................................................................................................................26

3.2 Materials and Methods .....................................................................................................29

3.2.1 Site description .......................................................................................................29

3.2.2 Experimental design and treatment description .....................................................31

3.2.3 Total dry biomass and above- and below ground C inputs ....................................32

3.2.4 Soil sampling and processing .................................................................................33

viii

3.2.5 Soil analysis ............................................................................................................33

3.3 Results...............................................................................................................................35

3.3.1 Soil organic C (SOC) and soil total N (STN) .........................................................35

3.3.2 Particulate and mineral-associated organic C (POC and MAOC) .........................40

3.4 Discussion .........................................................................................................................43

3.4.1 Changes in Soil organic C and soil total N .............................................................43

3.4.2 Changes in particulate and mineral-associated organic C ......................................49

3.5 Conclusions.......................................................................................................................51

Figures ....................................................................................................................................53

Tables ......................................................................................................................................60

CHAPTER 4 Sensitivity of Labile Soil Organic Carbon Pools and Enzymatic

Activities to Short-term Conservation Agriculture Cropping Systems ........................................76

Abstract ...................................................................................................................................76

4.1 Introduction .......................................................................................................................77

4.2 Materials and Methods .....................................................................................................81

4.2.1 Soil organic C pool extraction and analysis ...........................................................82

4.2.2 Assay of soil enzyme activities ..............................................................................85

4.2.3 Statistical analysis ..................................................................................................86

4.3 Results...............................................................................................................................86

4.3.1 Soil organic C pools (HWEOC, POXC, PEOC, CSOC) ........................................86

4.3.2 Soil enzyme activities (β-glucosidase and arylsulfatase) .......................................92

4.4 Discussion .........................................................................................................................94

ix

4.4.1 Changes in hot-water extractable organic C, permanganate

oxidizable C, pyrophosphate extractable organic C, and chemically

stabilized organic C .........................................................................................................94

4.4.2 Changes in β-glucosidase and arylsulfatase ...........................................................98

4.5 Conclusions.....................................................................................................................100

Figures ..................................................................................................................................101

Tables ....................................................................................................................................104

CHAPTER 5 Dynamics of Soil Aggregate-associated Organic Carbon under

Short-term Conservation Agriculture Cropping Systems ............................................................117

Abstract .................................................................................................................................117

5.1 Introduction .....................................................................................................................118

5.2 Materials and Methods ...................................................................................................121

5.2.1 Water stable aggregate .........................................................................................122

5.2.2 Distribution of water stable aggregates and soil aggregation indices ..................123

5.2.3 Concentrations of soil organic C, total N and permanganate

oxidizable C associated with aggregate size classes .....................................................123

5.2.4 Humic acid extraction and solid-state 13C-Nuclear Magnetic

Resonance (NMR) Spectroscopy ..................................................................................125

5.2.5 Statistical analysis ................................................................................................126

5.3 Results.............................................................................................................................127

5.3.1 Distribution of aggregate size classes and soil aggregate indices ........................127

5.3.2 Aggregate-associated soil organic C, total N and permanganate

oxidizable C, and C management index ........................................................................130

x

5.3.3 Relations between SOC associated with aggregate size classes and

soil aggregate indices ....................................................................................................133

5.3.4 Solid-state 13C-Nuclear Magnetic Resonance spectroscopy of

humic acid .....................................................................................................................134

5.4 Discussion .......................................................................................................................135

5.4.1 Effect of conservation agriculture on size distribution of water

stable aggregates and soil aggregation indices ..............................................................135

5.4.2 Effect of conservation agriculture on aggregate-associated SOC,

total N and POXC ..........................................................................................................138

5.5 Conclusions.....................................................................................................................143

Figures ..................................................................................................................................145

Tables ....................................................................................................................................146

CHAPTER 6 General Conclusions ..............................................................................................168

References ....................................................................................................................................170

xi

List of Figures

Figure 3.1. Location map of the research site. ...............................................................................53

Figure 3.2. Chronology of land use in the research site: (a) reference vegetation

and (b) experimental site. ...............................................................................................................53

Figure 3.3. Soil total N (STN) and soil organic C (SOC) concentrations in soils at

0- to 100-cm depths under different soil management and crop sequences in rice-

based cropping systems in (a) 2011 and (b) 2013..........................................................................54


0- to 100-cm depths under different soil management and crop sequences in

soybean-based cropping systems in (a) 2011 and (b) 2013.. .........................................................55


0- to 100-cm depths under different soil management and crop sequences in

cassava-based cropping systems in (a) 2011 and (b) 2013.. ..........................................................56

Figure 3.6. Particulate organic C (POC) and mineral-associated organic C

(MAOC) concentrations in soils at 0- to 100-cm depths under different soil

management and crop sequences in rice-based cropping systems in (a) 2011 and

(b) 2013 (only POC presented).. ....................................................................................................57



management and crop sequences in soybean-based cropping systems in (a) 2011

and (b) 2013 (only POC presented).. .............................................................................................58



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management and crop sequences in cassava-based cropping systems in (a) 2011

and (b) 2013 (only POC presented).. .............................................................................................59

Figure 4.1. Concentrations of (a) hot water-extractable organic C (HWEOC) and

permanganate oxidizable C (POXC), pyrophosphate extractable organic C and

chemically stabilized organic C (CSOC) in 2011, and (b) HWEOC and POXC in

2013 in 0- to 100-cm depth under rice- based cropping systems.. ..............................................101




(b) 2013 in 0- to 100-cm depth under soybean-based cropping systems.. ...................................102




2013 in 0- to 100-cm depth under cassava-based cropping systems.. .........................................103

Figure 5.1. CP/MAS 13C NMR spectra of humic acids extracted from large soil

macroaggregates (8-19 mm) under reference vegetation (RV), conventional tillage

(CT) and no-till (NT) in 0-5 cm depth. ........................................................................................145

xiii

List of Tables

Table 3.1 Land use, crop sequence and C input in the five-year experiment period

(2009-2013)....................................................................................................................................60

Table 3.2 Mineral fertilizer rates applied to crops during the experiment period

(2009–2013) ...................................................................................................................................61

Table 3.3 Soil attributes in 0- to 100-cm depths under reference vegetation and

experimental plots in 2011 .............................................................................................................62

Table 3.4 Soil bulk density (ρb) (Mg m-3) in 0- to 100-cm soil depths under

adjacent reference vegetation (RV), and rice- (RcCS), soybean- (SbCS) and

cassava- (CsCS) based cropping systems in 2011 .........................................................................63

Table 3.5 SOC stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-cm soil

depths under rice-based cropping systems .....................................................................................64

Table 3.6 Soil total N stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-

cm soil depths under rice-based cropping systems ........................................................................65


depths under soybean-based cropping systems..............................................................................66

Table 3.8 Soil total N stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-

cm soil depths under soybean-based cropping systems .................................................................67


depths under cassava-based cropping systems ..............................................................................68

Table 3.10 Soil total N stocks (Mg ha-1), on an equivalent soil-depth, in 0- to

100-cm soil depths under cassava-based cropping systems ..........................................................69

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Table 3.11 POC stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-cm

soil depths under rice-based cropping systems ..............................................................................70

Table 3.12 MAOC stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-cm

soil depths under rice-based cropping systems in 2011 .................................................................71


soil depths under soybean-based cropping systems .......................................................................72


soil depths under soybean-based cropping systems in 2011 ..........................................................73


soil depths under cassava-based cropping systems ........................................................................74


soil depths under cassava-based cropping systems in 2011...........................................................75

Table 4.1 Land use, crop sequence, and carbon input in the five-year experiment

period (2009-2013) ......................................................................................................................104

Table 4.2 Hot water-extractable organic C (HWEOC) stocks in 0- to 100-cm

depth under rice-based cropping systems at two sampling time (2011 and 2013) ......................105

Table 4.3 Permanganate oxidizable C (POXC) stocks in 0- to 100-cm depth

under rice-based cropping systems at two sampling time (2011 and 2013) ................................106

Table 4.4 Stocks of pyrophosphate extractable organic C (PEOC) and chemically

stabilized organic C (CSOC) in 0- to 100-cm depth under rice-based cropping

systems in 2011 ............................................................................................................................107

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depth under soybean-based cropping systems at two sampling time (2011 and

2013) ............................................................................................................................................108


under soybean-based cropping systems at two sampling time (2011 and 2013) .........................109

Table 4.7 Stocks of pyrophosphate extractable organic C (PEOC) and chemically

stabilized organic C (CSOC) in 0- to 100-cm depth under soybean-based cropping

systems in 2011 ............................................................................................................................110


depth under cassava-based cropping systems at two sampling time (2011 and

2013) ............................................................................................................................................111


under cassava-based cropping systems at two sampling time (2011 and 2013) ..........................112

Table 4.10 Stocks of pyrophosphate extractable organic C (PEOC) and

chemically stabilized organic C (CSOC) in 0- to 100-cm depth under cassava-

based cropping systems in 2011 ..................................................................................................113

Table 4.11 β-glucosidase and arylsulfatase activities at 0- to 20-cm depth under

rice-based cropping systems in 2011 ...........................................................................................114


soybean-based cropping systems in 2011 ....................................................................................115


cassava-based cropping systems in 2011 .....................................................................................116

xvi

Table 5.1 Land use, crop sequence, and C input in the three-year experiment

period (2009-2011) ......................................................................................................................146

Table 5.2 Distribution of aggregate size classes (g soil in aggregate fraction kg-1

soil) in reference vegetation (RV) and different treatments in rice-based cropping

systems .........................................................................................................................................147

Table 5.3 Mean weight diameter (MWD), mean geometric diameter (MGD) and

aggregate stability index (ASI) in reference vegetation (RV) and different

treatments in rice-based cropping systems ..................................................................................148


soil) in reference vegetation (RV) and different treatments in soybean-based

cropping systems ..........................................................................................................................149



treatments in soybean-based cropping systems ...........................................................................150


soil) in reference vegetation (RV) and different treatments in cassava-based

cropping systems ..........................................................................................................................151



treatments in cassava-based cropping systems ............................................................................152

Table 5.8 Concentrations of aggregate-associated SOC (g kg-1) in aggregate size

classes under rice-based cropping systems ..................................................................................153

xvii

Table 5.9 Concentrations of aggregate-associated total N (g kg-1) in aggregate

size classes under rice-based cropping systems ...........................................................................154

Table 5.10 Concentrations of aggregate-associated POXC (g kg-1) in aggregate

size classes under rice-based cropping systems ...........................................................................155

Table 5.11 C management index (CMI) of aggregate size classes under rice-

based cropping systems................................................................................................................156


classes under soybean-based cropping systems ...........................................................................157


size classes under soybean-based cropping systems ....................................................................158


size classes under soybean-based cropping systems ....................................................................159

Table 5.15 C management index (CMI) of aggregate size classes under soybean-



classes under cassava-based cropping systems ............................................................................161


size classes under cassava-based cropping systems.....................................................................162


size classes under cassava-based cropping systems.....................................................................163

Table 5.19 C management index (CMI) of aggregate size classes under cassava-


xviii

Table 5.20 Pearson correlation coefficients between aggregate-associated SOC

over size classes and soil aggregation indices under rice-based cropping systems .....................165


over size classes and soil aggregation indices under soybean-based cropping

systems .........................................................................................................................................166


over size classes and soil aggregation indices under cassava-based cropping

systems .........................................................................................................................................167

1

Abstract

Conservation agriculture (CA) constitutes a potential set of management practices to restore soil

total N (STN), soil organic C (SOC) and its labile fractions (i.e., particulate organic C-POC, hot-

water extractable organic C-HWEOC, permanganate oxidizable C-POXC), to increase soil

enzyme activities and to enhance soil aggregation. After five years, CA averagely increased SOC

stocks over CT at 0-5 cm by 10%, 20% and 18%, STN stock by 8%, 25% and 16% , POC stocks

by 22%, 20% and 78%, HWEOC stocks by 61%, 55% and 53%, and POXC stocks by 23%, 21%

and 32% for rice-, soybean- and cassava-based cropping systems (RcCS, SbCS and CsCS,

respectively). In general, no noticeable changes in the subsoil layers were observed. When

monitoring after three years, stocks of SOC fractions (i.e., mineral-associated organic C-MAOC,

pyrophosphate extractable organic C-PEOC, chemically stabilized organic C-CSOC) were

almost constant in each depth among land uses, except MAOC in SbCS and PEOC in CsCS at 0-

5 cm where CA showed significant effects. In contrast, β-glucosidase activity was 18%, 28% and

49% greater in CA than in CT soils at 0-5 cm under RcCS, SbCS, CsCS, respectively, whereas

arylsulfatase activity under CA was greater than CT by 36% in SbCS and 39% in CsCS. The

proportions of large macroaggregates (8-19 mm) at 0-5 cm under CA averagely increased 23%,

39% and 53% in RcCS, SbCS and CsCS, respectively, and consequently increased soil

aggregation indices (i.e., mean weight diameter-MWD, mean geometric diameter-MGD and

aggregate stability index-ASI) compared with those under CT. On average, and across all

aggregate size classes, CA accumulated SOC concentrations over CT by 11%, 7% and 6%, total

N concentrations by 3%, 11% and 15% and POXC concentrations by 18%, 20% and 15% for

RcCS, SbCS and CsCS, respectively, at 0-5 cm. These increases led to positive correlations

between large macroaggregate-associated SOC and soil aggregation indices in 0-5 cm depth in

2

the three cropping systems. The results of CP-MAS 13C NRM measurement showed that humic

acid from soils under CT tended to have higher proportions of aliphatic C than under CT while in

reverse for aromatic C. This supports the promotion of CMI under CA indicating greater lability

of SOC.

In conclusion, short-term CA practices in the three cropping systems increased the storage of

STN, SOC and labile SOC pool and enhanced soil enzyme activities in the surface soils with

potential effects in the subsoil layers through increased proportion of large macroaggregates and

soil aggregation indices resulting from high and diversified biomass-C inputs and the absence of

physical soil disruption.

3

CHAPTER 1

General Introduction

1.1 Research Justification

Soils can be either a source of or a sink for atmospheric CO2 depending on land use and

management (Lal, 2003b, 2010). Agricultural management practices play a substantial role in

soil organic C (SOC) dynamics (Chivenge, Murwira, Giller, Mapfumo, & Six, 2007; Lal, 1997;

Six et al., 2002). The SOC sequestration increase sustains soil quality and enhances crop

productivity (Lal, 2006; Reeves, 1997) due to its close association with a wide range of soil

processes and functioning (Smith, Petersen, & Needelman, 1999) including soil physical,

chemical and biological properties (Ayuke et al., 2011; Brévault, Bikay, Maldès, & Naudin,

2007; Lal, 2008b; Lienhard et al., 2013; Sá et al., 2009; Six, Bossuyt, Degryze, & Denef, 2004;

Tisdall & Oades, 1982). A decline in SOC due to the conversion of natural vegetation into

agricultural land is a common phenomenon (Lal, 2002). This decline results from a reduction in

organic matter inputs and soil physical disruption. Conventional tillage (CT) and crop residue

removal from agricultural land has been practiced for decades and detrimentally affects soil

productivity and sustainability (Farooq, Flower, Jabran, Wahid, & Siddique, 2011;

Franzluebbers, 2008; Govaerts et al., 2009). CT accelerates decomposition of young and

previously stable SOC through soil aggregate disruption that stimulates soil microbial biomass

and activity (D. Guo, Li, Li, Wang, & Fu, 2013; Reicosky, Kemper, Langdale, Douglas, &

Rasmussen, 1995; Sá et al., 2013; Shibu, Van Keulen, Leffelaar, & Aggarwal, 2010), and affects

soil drying and wetting (Six et al., 2004). Several studies have indicated SOC depletion under CT

in the tropical soils (Lienhard et al., 2013; Sá et al., 2013; Salinas-Garcia, Velazquez-Garcia, &

Rosales-Robles, 2000; Scopel, Findeling, Guerra, & Corbeels, 2005).

4

Conservation agriculture (CA) has been practiced for four decades and increasingly

adopted (Friedrich, Derpsch, & Kassam, 2012) to decrease annual expansion of soil degradation

and crop productivity loss. It holds tremendous potential to create sustainable agriculture based

on the application of its three key principles: (a) minimum mechanical soil disturbance (no-till)

restricted to sowing rows, (b) permanent soil cover by organic mulch, and (c) crop species

diversification (i.e. association or rotation) (FAO, 2008). SOC dynamics under CA systems are

driven by the balance between C inputs via crop residues and C outputs via microbial oxidation

(Davidson & Janssens, 2006; Lal, 2004b; Powlson, Prookes, & Christensen, 1987). It is

extremely difficult to substantially sequester SOC in arable soils without massive supplies of

organic materials (Powlson et al., 2011). These improved no-till (NT) practices in rotation or

association with diversified crop species that utilize more of the available growing periods aim to

enhance soil quality, restore SOC and increase crop productivity (Díaz-Zorita, Buschiazzo, &

Peinemann, 1999; Farooq et al., 2011; Govaerts et al., 2009; Sá et al., 2014) resulting from the

absence of soil aggregate disruption (Feller & Beare, 1997) and the increased amount, quality

and frequency of biomass-C inputs (Batlle-Bayer, Batjes, & Bindraban, 2010; Ogle, Breidt, &

Paustian, 2005; Ogle, Swan, & Paustian, 2012; Virto, Barré, Burlot, & Chenu, 2012) that create

positive C and N budgets and accentuate C and N transformation and flow (Boddey et al., 2010;

Sá et al., 2013). Greater SOC accumulation in tropical soils under NT cropping systems based on

a diversity of cash crops or in association with cover crops compared with CT has been reported

(Bayer, Martin-Neto, Mielniczuk, Pavinato, & Dieckow, 2006; Lienhard et al., 2013; Neto et al.,

2010; Sá et al., 2013; Scopel et al., 2005). SOC stored in the deeper soil layers may be in more

stable forms (Angers & Eriksen-Hamel, 2008) and its levels might be enhanced by the changes

in vegetation to deep-rooting crops that significantly affect the vertical distribution of SOC deep

5

in the soil profile, acting as a potential C sink (Jobbágy & Jackson, 2000). Séguy, Bouzinac, and

Husson (2006) reported that SOC in the subsoil was sequestered by higher SOC rhizodeposition

of the deep rooting systems such as Congo grass (Brachiaria ruziziensis), sorghum (Sorghum

bicolor) and Crotalaria spp. Thus, CA practices provide good support of SOC sequestration.

Short-term changes in total SOC as a result of soil management practices are often

difficult to detect (Zotarelli, Alves, Urquiaga, Boddey, & Six, 2007). To assess SOC dynamics

under short-term CA, it might be critical to separate SOC into fractions isolated by physical

(particulate organic C - POC, mineral-associated C - MAOC) and chemical (i.e., hot-water

extractable C - HWEOC, permanganate oxidizable C - POXC, pyrophosphate extractable

organic C - PEOC, chemically stabilized organic C - CSOC) methods and to monitor enzymatic

activities. Labile pools and enzymatic activities have recently received more attention due to

their sensitivity to short-term changes in soil management practices so they could be served as

sensitive indicators. The measurement of these fractions provides a good assessment of potential

SOC sequestration. Physical fractionation is a useful tool to interpret the SOC dynamics by

providing a rough differentiation between active, intermediate and passive SOC pools, and also

to assess the impact of soil management practices on dynamics (Cambardella & Elliott, 1994;

Christensen, 1992; Six, Elliott, & Paustian, 1999) and quantitative changes (Bayer, Martin-Neto,

Mielniczuk, & Ceretta, 2000) in SOC. In general, SOC is physically fractionated to two

fractions, POC and MAOC. POC is a sensitive C fraction to detect short-term changes in SOC

due to land use and management (Cambardella & Elliott, 1992; Freixo, Machado, dos Santos,

Silva, & Fadigas, 2002) whereas MAOC is a stable fraction related to SOC associated with silt-

and clay-size fractions (Bayer, Martin-Neto, Mielniczuk, Pillon, & Sangoi, 2001; Sá et al.,

2001). Tivet, Sá, Lal, Borszowskei, et al. (2013) indicated that the conversion of native

6

vegetation to cultivated land under CT reduced POC and MAOC fractions in a tropical red

Latosol while intensive NT cropping systems with diverse crop species association or rotation

restored these two physical size fractions of SOC. In addition to physical isolation, HWEOC,

POXC, PEOC and CSOC are chemically isolated. HWEOC constitutes the readily-decomposable

SOM (Ghani, Dexter, & Perrott, 2003) and responds rapidly to changes in C supply (Jinbo,

Changchun, & Wenyan, 2006). The dissolved organic C, microbial biomass C, soluble soil

carbohydrates and amines are all extracted from soil during the extraction of HWEOC (Ghani et

al., 2003). Similarly, POXC is also defined as labile SOC and related to soil microbial activity

including soil microbial biomass C (MBC), soluble carbohydrate C and total C (Weil, Islam,

Stine, Gruver, & Samson-Liebig, 2003). Several studies have found positive relationships

between MBC and HWEOC (Ghani et al., 2003; Ghani, Müller, Dodd, & Mackay, 2010;

Sparling, Vojvodić-Vuković, & Schipper, 1998), between MBC and POXC (Culman et al., 2010;

Melero, López-Garrido, Murillo, & Moreno, 2009) and between SOC and labile pools (i.e.

HWEOC and POXC) (Culman et al., 2012; Sá et al., 2014; Tirol-Padre & Ladha, 2004; Weil et

al., 2003). This labile SOC pool is enhanced by NT, cropping intensity and rotations and

increased SOC pool size. Soil enzymes involve in organic matter mineralization through a wide

range of metabolic processes in the soil system (María, Horra, Pruzzo, & Palma, 2002) by

providing information about microbial status and soil physicochemical conditions (Sinsabaugh et

al., 2008), and respond to soil management changes more quickly than other soil quality

indicators (Dick, 1994; Ndiaye, Sandeno, McGrath, & Dick, 2000). Arylsulfatase (EC 3.1.6.1)

involves in S cycling and catalyzes the hydrolysis of organic sulfate esters (M. A. Tabatabai &

Bremner, 1970). High organic C inputs constitute a principal reservoir of sulfate esters (Dick,

Pankhurst, Doube, & Gupta, 1997). β-glucosidase (EC 3.2.1.21) plays a role in the C cycle and is

7

closely related to the transformation and accumulation of organic matter (Wang & Lu, 2006).

These two soil enzymes are associated with NT and biomass-C inputs. Green, Stott, Cruz, and

Curi (2007) found that β-glucosidase activity were greater in NT soil compared with disk plow

soil in the tropical Savannah. Thus, the combination of SOC fractions and soil enzyme activities

under NT might provide the valuable information about the pathway to sequester C from the

atmosphere to soils and to decrease the release of SOC back to the atmosphere.

The increase in SOC stabilized in the soil under NT cropping systems may remain a great

potential for SOC sequestration. SOC stabilization is controlled by three main mechanisms: (a)

chemically innate recalcitrance, (b) protection through interaction with minerals, and (c)

occlusion in aggregates (Mikutta, Kleber, Torn, & Jahn, 2006). Soil aggregation has major effect

on soil C cycling, root development and soil resistance to erosion (Kay, 1998) and composes of

primary mineral particles and organic binding agents (Tisdall & Oades, 1982). The formation of

stable soil aggregates is related to mineralogy, texture (Feller & Beare, 1997) and SOC (Dutartre,

Bartoli, Andreux, Portal, & Ange, 1993; Tisdall & Oades, 1982). Aggregate-associated SOC

provides strength and stability, counters the impact of destructive forces and is an important

reservoir of soil C because of being physically protected from microbial and enzymatic processes

(Bajracharya, Lal, & Kimble, 1997). The continuous practices of CT damage soil structure by

breaking down soil aggregates (Zotarelli et al., 2007) and cause a reduction in the proportions of

soil macroaggregates and consequently exposing SOC to microbial oxidation. Thus, SOC

sequestration in soils under NT cropping systems is largely influenced by soil aggregation. NT

cropping systems in rotation or association with cover crops significantly enhances aggregate

stability, macroaggregates-occluded microaggregates and SOC protection compared with CT

(Barreto et al., 2009; Denef, Six, Merckx, & Paustian, 2004) due to their high biomass-C inputs

8

that generate a wide range of aggregating agents such as fungal hyphae, microbial bio-products

(Haynes & Francis, 1993), root exudates (Guggenberger, Frey, Six, Paustian, & Elliott, 1999)

and plant derived polysaccharides (Feller & Beare, 1997). This increased soil aggregate stability

through NT and aggregating agents enhances the ability of soil to protect and sequester SOC

leading to sustainable soil management.

The development of annual upland crops (i.e., maize, cassava, soybean and mungbean)

soared from ~ 217K ha in 2003 to ~ 716K ha in 2012 (MAFF, 2013) to satisfy the needs of

growing population in Cambodia. The agricultural land expansion for the production of these

crops has gradually diminished forest areas and exacerbated the growing concern over soil

degradation (Belfield, Martin, & Scott, 2013; Hean, 2004; Poffenberger, 2009; UNDP, 2010)

posing a serious threat to sustained agricultural productivity and food security (CDRI, 2014;

UNDP, 2010). Over 40% of the Cambodian population is affected by land degradation,

representing 78K km2 or 43% of total land area (Bai, Dent, Olsson, & Schaepman, 2008). The

figures might be higher in the last few years. The impacts of CA or its different component

practices have been reviewed to potentially sequester C into the agricultural soils in various

regions (Corsi, Friedrich, Kassam, Pisante, & Sà, 2012; Govaerts et al., 2009; Lal, 2006; Luo,

Wang, & Sun, 2010; Ogle et al., 2012). Thus, the challenges to apply this improved set of

agricultural management practices to sequester SOC and consequently to enhance soil and crop

productivity is necessary to define sustainable agriculture development.

1.2 Research Objectives

The effects of CA on SOC dynamics and its protection mechanisms in cropland soils in

Cambodia are still scarce so rigorous empirical evidence to fingerprint an appropriate soil

management practices and crop rotation scheme to promote SOC recovery is necessarily needed.

9

There might be no doubts that long-term CA can be a set of effective agricultural practices for

sequestering total SOC but short-term changes are still debatable. Therefore, this short-term CA

study was carried out (a) to assess the magnitude of changes in total SOC and its fractions (i.e.,

POC, MAOC, HWEOC, POXC, PEOC, CSOC) and soil enzymatic activities (i.e., arylsulfatase,

β-glucosidase) after conversion of RV to CT for five years and the potential of CA to recover

SOC close to an antecedent level under adjacent RV, and (b) to quantify the impacts of CA on

the SOC protection mechanism using aggregate size distribution and aggregate-associated total

SOC, total N and POXC after three-year practices, and the relationship between soil aggregation

indices and aggregate-associated SOC in three distinct upland cropping systems (i.e., rice,

soybean, cassava) in a savanna tropical agro-ecosystem of Cambodia.

1.3 Research Hypothesis

Given the above objectives, we hypothesized that the intensive NT systems (i.e., diversity

of cover/relay crops and high annual biomass inputs) within five years will be a starting step to

sequester SOC in the topsoil compared with CT in the three cropping systems by reducing

physical soil disruption and creating the C flow to support C storage, especially the labile SOC

pool and soil enzymes that will be served as indicators to estimate SOC dynamics over longer-

term trends. We expect that CT of heavy clayed Oxisols has a low impact on the original native

SOC stocks and its SOC level will be slightly increased due to the spread of crop residues after

harvesting main and preceding crops. This research will also test the hypothesis that increasing

soil aggregate stability and enhancing the formation of large macroaggregates by intensive NT

systems is the underlying mechanism driving SOC dynamics and is judicious management

strategy leading to increased aggregate-associated SOC, total N and POXC compared with CT

through continuous provision of aggregate binding agents from crop residues.

10

CHAPTER 2

Literature Review

2.1 Conservation Agriculture Practices and Adoption

The continuous application of CT practices with crop residue removal from agricultural

land has been implemented for decades causing negative effects on soil productivity and

sustainability (Farooq et al., 2011; Franzluebbers, 2008; Govaerts et al., 2009) by increasing CO2

emission to the atmosphere and lowing the total C sequestration held within the soil, thus cannot

ensure the sustainable management of agro-ecosystems. Over the last few decades, a general

trend in the soil degradation has been noticed, which is one of the most pertinent constraints

occurring in agricultural land causing a reduction of soil’s actual and potential productivity and

posing a serious threat to agricultural sustainability and environmental quality (Lal, 1993). In

intensified cropping systems, CT and inadequate organic matter inputs have a heavy toll on

maintaining the soil integrity. The challenges to develop appropriate agricultural management

practices to sequester soil C and sustain soil and crop productivity have become more intense in

recent years. The secret to combat soil degradation leading to sustainable agriculture is to never

allow the soil to be bare and unprotected, but to ensure that the soil surface is always covered

with growing plants or the dead mulch (Brown, 2008).

Conservation agriculture (CA) has been practiced for about four decades and spread

widely (Friedrich et al., 2012) and has become a hegemonic paradigm in sustainable agricultural

development because it constitutes the effective tool to create sustainable crop production

intensification with its three key principles: (a) minimum mechanical soil disturbance (no-till),

(b) permanent soil cover by organic mulch and (c) diversified crop species rotation or association

(FAO, 2008). These principles seem to be applicable to a wide range of crop production systems

11

from low-yielding, dry rainfed to high-yielding irrigated conditions (Govaerts et al., 2009). CA

has been practiced to decrease the expansion of soil degradation and crop productivity loss while

conserving the environment. This improved set of agricultural management practices that utilizes

more of the available growing periods aims to restore SOC and enhances soil and crop

productivity (Díaz-Zorita et al., 1999; Farooq et al., 2011; Govaerts et al., 2009; Sá et al., 2014)

resulting from the absence of soil aggregate disruption (Feller & Beare, 1997) and the increased

amount, quality and frequency of biomass-C inputs via crop residues (Batlle-Bayer et al., 2010;

Ogle et al., 2005; Ogle et al., 2012; Virto et al., 2012). Based on its capability of building

sustainability into agricultural production systems, the adoption of CA or its components is

increasing in several parts of the world as an alternative to both conventional and organic

agriculture. According to global assessments of available figures, CA or (at least) NT systems

have been increasingly adopted on total cultivated land areas of ~ 72 million ha in 2001 (Derpsch

& Benites, 2003), ~ 96 million ha in 2004 (Derpsch, 2005), ~ 106 million ha in 2008 (Derpsch &

Friedrich, 2009) and ~ 155 million ha in 2013 (FAO, 2014). The majority of the adopted land

areas are in South America and North America while it is limitedly adopted in Africa and Asia

where small holder, resource-constrained farmers are dominated in these two continents. The

applicability and adoption of CA are most likely to succeed in large-scale rather than small-scale

farming. In the case of Africa, Giller, Witter, Corbeels, and Tittonell (2009) argued that the

scientific evidence to empirically support the claims made for CA is inconclusive, and that CA

does not fit within the majority of current smallholder farming systems in Africa. These

constraints might also exist in Asia, particularly in Cambodia where CA is a relatively new

concept and has been introducing to sustainably intensify crop production while having

considerable environmental benefits.

12

In Cambodia, the land expansion for upland crop production (i.e., maize, cassava,

soybean and mungbean) soared from ~ 217K ha in 2003 to ~ 716K ha in 2012 (MAFF, 2013)

due to rural population growth and has gradually diminished forest areas and exacerbated the

growing concern over soil degradation (Belfield et al., 2013; Poffenberger, 2009; UNDP, 2010).

Most identified soil types have a rather low natural fertility and a process of soil degradation is

apparent (Johnsen & Munford, 2012). Over 40% of the Cambodian population was affected by

land degradation, representing 78,000 km2 or 43% of total land area (Bai et al., 2008). CT and

high inputs of chemical fertilizers and pesticides have been widely implemented to intensify

upland crop production in the country and have increased the expansion of degraded upland

soils, which might lead to an increase in the total degraded land area in the last few years. Thus,

the increasing concern over the long-term ecological and economic impacts has been raised for

sustainable crop and soil management. To combat the loss of agricultural productivity in the

uplands, to ensure sustainability of agronomic land use and to intensify crop production, the

Ministry of Agriculture, Forestry and Fisheries of Cambodia under the support of Agence

Française de Développement (AFD) and Centre de Coopération Internationale en Recherche

Agronomique pour le Développement (CIRAD) initiated a research and development (R&D)

program on direct seeding mulch-based cropping (DMC) systems to create and propose

sustainable intensification of upland cropping systems in the country (Boulakia, Kou, San, Leng,

& Chhit, 2008). DMC is a promising option of sustainable soil management in the tropics due to

the absence of soil disruption and the permanent soil cover by a mulch of crop residues (Scopel

et al., 2005). DMC systems are now gathered under the broad concept of CA. This set of

agricultural management practices provides a significant effect on soil processes and functioning

under intensified cropping systems in the upland area of Cambodia where soils intensively

13

plowed and cropped and the majority of crop residues removed from the fields. The R&D

program has implemented in the 45 ha land area in Bos Khnor Research Station and expanded to

smallholder farmers in Battambang and Kampong Cham provinces covering 375 ha of upland,

rainfed cropping systems (SOFRECO, 2013). Although CA ensures sustainable crop production

intensification through soil quality improvement and SOC sequestration, the adoption of CA

among farmers in Cambodia is still limited due to lack of knowledge, capital intensive and

conclusive empirical evidence.

2.2 Contribution of Tillage-induced Soil Carbon Loss to Global Warming

The marked increase in greenhouse gas emissions in recent years has been considered as

a serious threat to global warming. The present atmospheric CO2 increase is dominantly caused

by anthropogenic emissions of CO2. Total anthropogenic emissions of C as CO2 were 6.3 Pg yr-1

during the 1980s, 8.0 Pg yr-1 during the 1990s and about 9.0 Pg yr-1 between 2000 and 2005 (Lal

& Follett, 2009). Soil is one of important natural reservoirs of C and it was estimated to have

contributed as much as 55 to 878 Pg of C to the total atmospheric CO2 (Kimble, Lal, & Follett,

2002). The CO2 emiision from the soil is the second largest component of the global C cycle and

contributes to climatic variation (Reth, Reichstein, & Falge, 2005). The conversion of natural

ecosystems into agricultural ecosystems and soil cultivation typically depletes SOC (Don,

Schumacher, & Freibauer, 2011; L. B. Guo & Gifford, 2002; Sá et al., 2013; Wei, Shao, Gale,

Zhang, & Li, 2013) with the attendant emission of CO2 into the atmosphere (Lal & Follett,

2009). The increasing annual release of CO2 contributes growing concerns over global warming

and leads to increased strong interest in the role of soils to store C to counterbalance this rising

atmospheric CO2 levels mitigating the risks of global warming.

14

Soils can act as either a sink for or a source of atmospheric CO2 depending on the

changes in land management practices (Lal, 2003b, 2010). Agricultural management practices

profoundly affect SOC dynamics (Chivenge et al., 2007; Lal, 1997; Six et al., 2002). SOC is

considered to play a key role in sustaining soil and crop productivity (Lal, 2006; Reeves, 1997)

and controlling belowground system stability (S. Huang, Sun, Rui, Liu, & Zhang, 2010) due to

their effects on soil physical (Bhogal, Nicholson, & Chambers, 2009; Guzman & Al-Kaisi, 2011;

Tisdall & Oades, 1982), chemical (Hao, Chang, & Lindwall, 2001; Sá et al., 2009), and

biological (Ayuke et al., 2011; Bhogal et al., 2009; Brévault et al., 2007; Lienhard et al., 2013;

Six et al., 2004; Uphoff et al., 2006) properties. Thus, its loss negatively impacts the soil

structure leading to compaction while increasing CO2 flux from soils to the atmosphere (Bronick

& Lal, 2005) and thus affect the global C balance. The tillage of forest or natural grassland soils

after conversion to cropland results in the considerable loss of 55 Pg of C from the global SOC

pool thereby converted a large fraction of SOC to CO2 (Pacala & Socolow, 2004). Soil

cultivation by continuous CT causes the increased decomposition rate of previously stable SOC

due to physical soil disruption that greatly exposes young and stable C to the microbial attack

(Lienhard et al., 2013; Reicosky et al., 1995; Sá et al., 2013; Shibu et al., 2010). In addition, CT

results a higher contact between soil and crop residues and increases soil temperature, favoring

organic matter decay and consequently increasing the CO2 emission from the soils (La Scala,

Lopes, Marques Jr, & Pereira, 2001; Lal, 2003a). Numerous studies in various regions showed

the higher CO2 emission from soils under CT in relation to NT (Al-Kaisi & Yin, 2005; Carvalho

et al., 2009; Franchini, Crispino, Souza, Torres, & Hungria, 2007; Fuentes et al., 2012; Liu et al.,

2013; Omonode, Vyn, Smith, Hegymegi, & Gál, 2007; Ruan & Philip Robertson, 2013; Ussiri &

Lal, 2009). The study of Carvalho et al. (2009) in a very clayed Oxisol in the humid tropics

15

indicated that CT systems had 20% and 22% higher CO2 emission rates (i.e., soil and root

respiration) in dry and wet seasons, respectively, compared with NT, which resulted from

disruption of the structural integrity of soil aggregates under CT, accelerating organic matter

oxidation and thus increasing CO2 flux from CT soils to the atmosphere. They emphasized the

potential of NT systems to mitigate CO2 emissions from the soils. In summary, CT exposes more

soil to the air, causing SOC to react and escape as CO2 that may exacerbate the global warming.

This CO2 efflux from soils to the atmosphere results from root respiration and physiological

processes of microorganisms involved in the organic matter decomposition.

2.3 Soil Carbon Sequestration under Conservation Agriculture

SOC sequestration is the process of transferring atmospheric CO2 into the soil through

crop residues and other organic solids, and in a form that is not immediately reemitted (Olson,

2013; Osman, 2013). SOC sequestration by agricultural land has generated global interest due to

its potential impact and benefits for both agriculture and climate change adaptation and

mitigation (Olson, Al-Kaisi, Lal, & Lowery, 2014). The nature of NT cropping systems (i.e.,

cropping sequence, use of relay/cover crops, crop frequency in the sequence) and the variability

in biomass-C inputs (i.e., quantity and quality) are the main control of SOC sequestration (Ogle

et al., 2005). The persistent practices of conventional farming based on intensive tillage have

magnified SOC depletion and detrimental impacts on the crop productivity and environment.

SOC loss from the tropical soils through physical soil disruption by CT has been reported

(Lienhard et al., 2013; Nascente, Li, & Crusciol, 2013; Sá et al., 2013; Salinas-Garcia et al.,

2000; Scopel et al., 2005). Soil C degradation leads to soil quality losses and poses a threat for

both agricultural production systems and food security (Lal, 2004a). Concerning over these

issues, CA has been adopted for the ultimate vision of sustainable crop production intensification

16

while conserving the soils. In this context, SOC sequestration plays a major role in maintaining

soil and crop productivity due to its effect on soil properties. SOC dynamics under CA systems

are driven by the balance between C inputs via crop residues and C outputs via microbial

oxidation (Davidson & Janssens, 2006; Lal, 2004b; Powlson et al., 1987). Thus, SOC can be

sequestered by crop rotations and NT practices with addition of crop residues near the soil

surface (Lal, Follett, & Kimble, 2003). SOC might be not only sequestered in the top soil layer

but also in the subsoil layers when deep-rooting cover crops are included in the crop rotations.

Séguy et al. (2006) reported that SOC in the subsoil could be increased by higher SOC

rhizodeposition of the deep rooting systems such as Congo grass, sorghum and Crotalaria spp.

These cover crops are well adapted to acidic soils and produce high crop biomass which

becomes part of the soil as SOC pool. Therefore, investigations of SOC dynamics of agricultural

tropical soils can provide valuable information on how to promote C sequestration in such soils

(Bayer, Martin-Neto, et al., 2006).

Several studies have been proved that the practices of CA or its components significantly

restore SOC. Scopel et al. (2005) studied the five-year impacts of CA with varying levels of

surface crop residues retained on the soil surface on changes in soil C dynamics in a semi-arid

tropical climate. The results showed that CA treatments accumulated significantly high C

concentrations compared with CT at 0-2.5 and 2.5-5 cm soil depths. On average of the two CA

with crop residue inputs, and across the two surface soil layers, soils under CA had 77% higher

C concentrations than that under CT. CA did not show a significant effect at deeper layers. This

was probably due to a short-term period and limited C inputs. In addition, the increase in mulch

inputs of maize residues from 1.5 to 4.5 Mg ha-1 into the soil resulted in a higher C accumulation

of 29% at 0-2.5 cm soil layer. After corrected differences in bulk density, the two CA treatments

17

averagely had 26% (4.75 Mg C ha-1) more C stocks than CT at 0-20 cm depth. This result

suggests that biomass-C inputs from crop residues can increased the level of soil C levels over a

five-year period compared with CT at 0-5 cm of the soil profile. Similarly, Sá et al. (2013)

investigated the effects of NT with diverse biomass-C inputs from residues of various crop

species included in the crop rotations on SOC dynamics among native vegetation (NV), NT and

CT cropping systems in a tropical Oxisol. After eight years, SOC stock under CT was 30% (14.2

Mg C ha-1) lower than that under NV whereas NT (average of the six NT treatments) stored SOC

20% (6.7 Mg C ha-1) higher than CT in the 0-20 cm soil depth. The increase in SOC in deeper

was also observed in soils under NT. Considering 100 cm as a single stratum, soils under NT had

12% (12 Mg C ha-1) higher SOC stocks than that of CT. The SOC stock levels were in an order

NV > NT > CT. They concluded that SOC accumulation increased with increaing bimass-C

inputs and it was evident that intensive NT cropping systems (high and diversified annul C

inputs) had a potential to restore SOC proviously deplted by CT in the stuided tropical climate.

The adoption of CA systems increases above- and belowground biomass-C inputs and decreases

SOC decomposition rates through increased soil aggregation to protect SOC from decomposers,

which leads to SOC sequestration.

To better understand the impacts of agricultural management practices on SOC dynamics,

it is necessary to separate SOC into fractions isolated by physical and chemical methods and to

also assay soil enzyme activities. These SOC fractions can provide valuable information to

estimate SOC dynamics over long-term trends. The SOC pool is highly diverse with contrasting

turnover times, and stabilized or protected again microbial decomposition (Lützow et al., 2006).

The labile SOC pool (i.e., POC, HWEOC, POXC) is the most rapid turnover times and

potentially restored even in a short period. This pool is likely to be more sensitive to soil

18

management practices than total SOC (Campbell, Janzen, & Juma, 1997; Z. Huang, Xu, & Chen,

2008). Physical fractionation is a useful tool to interpret the SOC dynamics by roughly

differentiating active, intermediate and passive SOC pools, and to assess the impact of soil

management on dynamics (Cambardella & Elliott, 1994; Christensen, 1992; Six et al., 1999) and

quantitative changes (Bayer et al., 2000) in SOC. Soil particle-size fractionation plays a crucial

role in assessing the soil organic matter (SOM) accessibility (Gregorich, Beare, McKim, &

Skjemstad, 2006) and interactions between organic and inorganic soil components in the

turnover of SOM (Christensen, 1992, 2001). POC is a labile fraction and a good qualitative

indicator to detect changes in SOM due to land use and management (Cambardella & Elliott,

1992; Freixo et al., 2002). POC is a sensitive pool and its changes are directly related to the

quantity, quality and frequency of crop residues added to the soil (Diekow et al., 2005; Lienhard

et al., 2013; Sá et al., 2001; Vieira et al., 2007). In contrast, MAOC is considered as a stable

fraction and less sensitive than POC to land use and management. It reflects the relationship

between SOC and silt- and clay-size fractions (Bayer et al., 2001; Sá et al., 2001). It can be

changed by physical and chemical soil environment rather than by land use changes

(Guggenberger, Christensen, & Zech, 1994) resulting in a lower turnover rate (Feller & Beare,

1997). Results from the study of Tivet, Sá, Lal, Borszowskei, et al. (2013) to assess the

magnitude of changes SOC fractions (i.e., POC, MAOC) in a red tropical Latosol indicated that

intensive NT cropping systems increased POC and MAOC stocks compared with CT in the 0-20

cm soil layer after eight years. On average, POC and MAOC stocks under NT treatments were

19% (~ 1.3 Mg POC ha-1) and 13% (~ 3.5 Mg MAOC ha-1) higher, respectively, than those under

CT. When comparing with NV, the loss rate of POC and MAOC under CT in the 0-20 cm was

revealed whereas NT systems showed a recovery trend of POC and MAOC compared with the

19

antecedent levels under NV. They emphasized that NT systems with high biomass-C inputs from

crop residues potentially restore POC and MAOC fractions previously depleted by CT.

Labile SOC fractions isolated by the chemical method (i.e., HWEOC, POXC) are more

sensitive to agricultural management practices than PEOC and CSOC and respond quickly to

changes in C supply. The dissolved organic C, MBC, soluble soil carbohydrates and amines are

all extracted from soil during the extraction of HWEOC (Ghani et al., 2003). POXC is also an

active SOC pool and it is known that slightly alkaline KMnO4 is used to hydrolyze and oxidize

simple carbohydrates, amino acids, amine/amine sugars, and C-compound containing hydroxyl,

ketone, carboxyl, double-bond linkages and aliphatic compounds (Loginow, Wisniewski, Gonet,

& Ciescinska, 1987). Positive relationships between MBC and HWEOC (Ghani et al., 2003;

Ghani et al., 2010; Sparling et al., 1998), between MBC and POXC (Culman et al., 2010; Melero

et al., 2009; Weil et al., 2003) and between SOC and labile pools (i.e., HWEOC and POXC)

(Culman et al., 2012; Sá et al., 2014; Tirol-Padre & Ladha, 2004; Weil et al., 2003) have been

reported. Thus, the increase in these labile SOC pools can be the pathway to sequester SOC.

Pyrophosphate is used to extract soil C due to its selective ability to remove Fe and Al-bound

organic matter by complexing with di- and trivalent cations (Wattel-Koekkoek, van Genuchten,

Buurman, & van Lagen, 2001). Thus, PEOC pool represents the SOC associated with the active

forms of Fe and Al. CSOC is known as the passive or refractory SOM pool is organic substances

which is resistant to further mineralization (Eusterhues, Rumpel, & Kögel-Knabner, 2005).

These two SOC pools are less impacted by short-term land use and management. Tivet, Sá, Lal,

Borszowskei, et al. (2013) reported that NT systems associated or rotated with diverse cover crop

species had 59% (0.22 Mg C ha-1) higher HWEOC stocks than CT in the 0-20 cm depth in a red

tropical Latosol after eight years. The higher stocks were also observed in the deeper soil layers

20

but the significant differences in CSOC stocks were not detected between CT and NT. Stine and

Weil (2002) studied the change in POXC concentration under CT and NT in a tropical region of

south central Honduras. They found that soils under NT contained 76% higher POXC than CT

and POXC was highly correlated with total soil C. They emphasized that changes in total soil C

resulted from proportional changes in both active and passive C fractions. In addition, the results

also showed the positive correlation between macroaggregate stability and POXC concentration.

Thus, less soil disruption and higher biomass-C inputs under NT systems contributes to the

greater HWEOC and POXC and consequently enhance soil macroaggregate formation which

may protect SOC (Tivet, Sá, Lal, Briedis, et al., 2013). The enzyme activities in soil systems

vary primarily due to different amounts of organic matter content and composition, living

organisms’ activity and intensity of biological processes (Das & Varma, 2011). They are

sensitive indicators to provide valuable information on the impact of land use management and

cropping systems (Fernandes, Bettiol, & Cerri, 2005; Rabary et al., 2008). Arylsulfatase plays an

important role in S cycling and can catalyze the hydrolysis of organic sulfate esters (M. A.

Tabatabai & Bremner, 1970) while β-glucosidase in the C cycle ant it is closely related to the

transformation and accumulation of SOM (Wang & Lu, 2006). Green et al. (2007) studied the

impact of tillage practices on soil biological activity in a red Latosol in the tropical Savannah.

They found that β-glucosidase activity in the soil under the NT corn-common bean (Phaseolus

vulgaris L.) rotation was 82% significantly greater than under disk plow management in the 0-5

cm depth after five years and emphasized that β-glucosidase activity in the topsoil was sensitive

to soil management practices. Together with other soil enzymes in their study, it was concluded

that NT management improved soil biological properties leading to soil aggregate stabilization.

High biomass-C inputs constitute a principal reservoir of sulfate esters, the substrate for

21

arylsulfatase (Dick et al., 1997). The study in a temperate soil by Gajda, Przewłoka, and

Gawryjołek (2013) reported the arylsulfatase activity under eight-year NT systems was two- to

threefold greater than that obtained under traditional tillage at 0-15 cm soil layer resulting from

higher plant residue inputs. They also found a positive correlation between MBC and

arylsulfatase activity. It is evident that NT and biomass-C inputs via crop residues significantly

affect these two soil enzymes particularly in the soil surface and consequently enhance soil

aggregation that is an important mechanism to increase SOC sequestration.

In conclusion, the continuous inputs of biomass-C via crop residues to the soil surface

under NT cropping systems potentially restore SOC, its fractions and soil enzyme activities

which can be used as good indicators of sustainable soil management.

2.4 Soil Aggregate Stability and Soil Carbon Sequestration

Soil aggregates are composed of primary mineral particles and organic binding agents

(Tisdall & Oades, 1982). Soil aggregation has major effect on soil C cycling, root development

and soil resistance to erosion (Kay, 1998) and it one of important mechanisms to protect and

sequester SOC (Feller & Beare, 1997; Lützow et al., 2006). The formation of stable soil

aggregates is related to mineralogy, texture, the quality and quantity of organic matter inputs,

exchangeable ions, aluminum and iron oxides, SOC concentration and microbial activities

(Bronick & Lal, 2005; Feller & Beare, 1997; Kay, 1998). The proportions of soil water stable

aggregates often change rapidly when tillage practices and crop rotations are modified (Angers,

Pesant, & Vigneux, 1992). SOC sequestration through aggregation is an important aspect of soil

management. The SOC in microaggregates is believed to be protected from degradation and

hence relevant for C sequestration. Thus, soil aggregation and SOC accumulation due to physical

protection are two intrinsically linked phenomena (Barreto et al., 2009). Aggregate-associated

22

SOC provides strength and stability and is an important reservoir of soil C because of being

physically protected from microbial and enzymatic degradation (Bajracharya et al., 1997). A

positive relationship between SOC and soil aggregate stability has been reported in studies from

various regions (Briedis, Sá, Caires, Navarro, et al., 2012; Dutartre et al., 1993; Madari,

Machado, Torres, de Andrade, & Valencia, 2005; Tisdall & Oades, 1982; Tivet, Sá, Lal, Briedis,

et al., 2013). Therefore, soil aggregate stability indicates the ability of soil to sequester SOC and

might be used as an indicator of sustainable soil management practices.

The frequent CT reduces the proportions of stable macroaggregates by breaking down

soil aggregates (Zotarelli et al., 2007) and hastens SOC oxidation through stimulation of soil

microbial biomass and activity (D. Guo et al., 2013; Six et al., 2004) thus resulting in high SOC

humification degree (Balesdent, Chenu, & Balabane, 2000; Bayer et al., 2001; Six, Elliott, &

Paustian, 2000) which reduces SOC storage (Bidisha, Joerg, & Yakov, 2010; Ogle et al., 2012).

SOC sequestration in soils under NT systems is largely influenced by aggregation (Six et al.,

2000). Soil aggregate stability is a function of the liberation of aggregating agents, principally by

microorganisms, through the decomposition of organic residues (Cosentino, Chenu, & Le

Bissonnais, 2006). NT has less deleterious effects on soil structure (Lal & Kimble, 1997) and

provides the constant inputs of organic materials to generate a range of aggregating agents such

as fungal hyphae, microbial bio-products (Haynes & Francis, 1993) and root exudates

(Guggenberger et al., 1999). The improved soil aggregation through NT can enhance the

physical protection of SOC against losses due either to mineralization or detachability and

erosion (Feller & Beare, 1997). It has been widely observed that NT with crop rotations have

significantly higher aggregate stability, a greater protection of SOC (Barreto et al., 2009; Castro

Filho, Lourenço, de F. Guimarães, & Fonseca, 2002; Denef et al., 2004; Madari et al., 2005), and

23

larger aggregates and larger proportion of the soil in greater aggregate size classes (Barreto et al.,

2009; Madari et al., 2005; Tivet, Sá, Lal, Briedis, et al., 2013) compared with those of CT. Tivet,

Sá, Lal, Briedis, et al. (2013) investigated the impacts of CT and NT on aggregate size

distribution and aggregate-associated SOC in a red tropical Latosol. After eight years, the results

indicated that the proportions of large macroaggregates (8-19 mm) decreased from 50% under

NV to 35% under CT, and ranged from 33% to 51% under intensive NT cropping systems (high

and diversified annual C inputs) in the 0-20 cm soil depth. Consequently, soil under CT had

higher amounts meso- and microaggregates, indicating the disruptive effect of CT on aggregate

size distribution. SOC stocks in the large macroaggregate fraction represented 52%, 37% and

41% of the total SOC stocks across all aggregate size under NV, CT and NT, respectively. The

positive correlation between aggregate-associated SOC concentrations and labile SOC was also

reported. It was concluded that NT with diverse biomass-C inputs increased SOC and reformed

the largest macroaggregates that is crucial to SOC storage and stabilization. Similarly, Madari et

al. (2005) studied the effects of NT and crop rotations on soil aggregation and SOC dynamics in

a Rhodic Ferralsol in the subtropical climate. They found that the conversion of forest to

cultivated land reduced the proportions of large macroaggregate (8-19 mm) by 70% and 32% at

0-5 cm depth under CT and NT, respectively. The aggregate-associated SOC under NT was

greater than that under CT in the eight size classes in the top soil layer (0-5 cm). It was

concluded that NT with crop rotations enhanced soil macro-aggregation and aggregate-

associated SOC in the 0-5 cm soil depth due to the absence of soil disturbance and higher

biomass-C inputs to aggregates through a slower macroaggregate turnover rate. An improvement

of soil aggregate stability under NT with high biomass-C inputs protects the enzymatic and

24

microbial attacks and significantly contributes to SOC stabilization in aggregates leading to long-

term C sequestration (Balesdent et al., 2000).

In conclusion, the adoption of CA or its components contributes to the restoration of SOC

and its fractions previously destroyed by CT through the continuous biomass-C inputs (i.e., plant

roots, root exudates, aboveground residues) which maintain C flow in the soil and the absence of

physical disruption. Consequently, soils under CA have greater aggregate satiability and favor

the formation of macroaggregates leading to SOC stabilization within aggregates which

potentially protects SOC within macroaggregate-occluded microaggregates in the soil profile.

25

CHAPTER 3

Short-term Conservation Agriculture Impacts on Total, Particulate and Mineral-

associated Soil Organic Carbon in a Savanna Tropical Agro-ecosystem

Abstract

Conservation agriculture (CA) is an effective tool that is used to increase soil C

sequestration and enhance soil quality and agronomic productivity. However, rigorous empirical

evidence from Southeast Asia, particularly in the Cambodian agro-ecosystem, is still scarce. The

aim of this study was to quantify the short-term (i.e., five year) impacts of soil management and

cropping systems on soil organic C (SOC), soil total N (STN), particulate organic C (POC) and

mineral-associated organic C (MAOC). There were three distinct experiments comprised of a

combination of cover and main crops, including rice-, soybean- and cassava-based cropping

systems, hereafter designated as RcCS, SbCS and CsCS, respectively. The experimental plots

were laid out in a randomized complete block design with three replicates. Soil management

treatments included conventional tillage (CT) and no-till (NT) and a selected adjacent area of the

reference vegetation (RV). Soil sampling was conducted in 2011 and 2013 at seven depths (0-5,

5-10, 10-20, 20-40, 40-60, 60-80 and 80-100 cm). Soil management and crop sequences

significantly affected SOC and STN stocks in all three cropping systems. On average, NT

increased SOC stocks at 0-5 cm depth over those of CT by 10%, 20% and 18% and STN stocks

by 8%, 25% and 16% for RcCS, SbCS and CsCS, respectively. SOC levels followed the order

RV > NT >CT. SOC stocks in the subsoil layers were consistently in NT than in CT in all three

cropping systems. POC stocks at 0-5 cm depth in NT were on average 22%, 20% and 78%

greater than those in CT in RcCS, SbCS and CsCS, respectively. However, significant

differences were detected only in RcCS and CsCS. The major POC stocks were found at 0-20

26

cm depth. NT treatments in SbCS stored 9% greater MAOC stocks at 0-5 cm depth than those in

CT, and an increasing trend of NT was observed in RcCS and CsCS. In all three cropping

systems, NT systems with diversified crop significantly affected SOC and POC stocks in the

surface soils and tended to restore SOC and POC in the subsoil layers after five years. The

results agree with the observation that short-term CA associated with high biomass-C inputs

(particularly bi-annual rotations) promotes SOC recovery in the topsoil layer and creates a

potential to increase SOC in the subsoil layers when deep-rooting cover crops are included in

crop rotations.

3.1 Introduction

Agricultural land expansion for crop production, due to rural population growth, has

gradually diminished forest area and exacerbated the growing concern over soil degradation in

Cambodia (Belfield et al., 2013; Hean, 2004; Poffenberger, 2009; UNDP, 2010). The

development of annual upland crops (i.e., maize, cassava, soybean and mung bean) soared from

217,106 ha in 2003 to 716,370 ha in 2012 (MAFF, 2013). Currently, their production has

become an important component of smallholder agriculture development in the western and

northern regions of the country, although negative impacts on natural resources and farm

economy are already noticeable. Most soil types identified have a rather low natural fertility, and

the process of soil degradation is apparent in most parts of the country (Johnsen & Munford,

2012). Soil degradation reduces the productivity of arable land and poses a serious threat to

sustained agricultural productivity and food security (CDRI, 2014; UNDP, 2010). Over 40% of

the Cambodian population is affected by land degradation, which represents 78,000 km2 or 43%

of total land area (Bai et al., 2008). Despite substantial growth of various sectors, Cambodia’s

economy is still predominantly agrarian. The agricultural sector contributed close one-third of

27

Cambodia’s GDP in recent years and employed more than half of the country’s total labor force

(Yu & Diao, 2011). Thus, the country is faced with a challenge to sustainably increase crop

productivity while conserving soil quality and protecting the environment. Continuous

conventional plow-based tillage practices and crop residue removal from agricultural land have

been implemented for decades and have negative effects on soil productivity and sustainability

(Farooq et al., 2011; Franzluebbers, 2008; Govaerts et al., 2009). These practices cause increased

decomposition of previously stable soil organic matter (SOM) due to physical soil disruption and

greater exposure of young and stable C to microbial attack (Reicosky et al., 1995; Sá et al.,

2013). Land use and agricultural management practices such as tillage, mulching and crop

residue management influence soil organic carbon (SOC) dynamics (Chivenge et al., 2007; Lal,

1997; Six et al., 2002). SOC plays a crucial role in sustaining soil quality and crop productivity

(Lal, 2006; Reeves, 1997) due to its profound influence on soil properties (Brévault et al., 2007;

Lienhard et al., 2013; Sá et al., 2009; Tisdall & Oades, 1982). A decline in SOC due to the

conversion of natural forest or native vegetation into cropland is a common phenomenon (Lal,

2002). This decline results from a reduction in total organic C inputs and an increase in

decomposition rate (Shibu et al., 2010; Tivet, Sá, Lal, Borszowskei, et al., 2013). Sá et al. (2013)

report that SOC stock of 0.67 Mg C ha-1 year-1 at a 0-20 cm depth was depleted after eight years

of conversion from native vegetation to agricultural land using a continuous plow-based tillage

in a tropical region (i.e., Cerrado) of Brazil.

SOC dynamics under conservation agriculture (CA) systems are driven by the balance of

C inputs (via crop residues) and C outputs (via microbial oxidation) (Davidson & Janssens,

2006; Lal, 2004b; Powlson et al., 1987). NT cropping systems based on high diversity and

biomass-C inputs, which utilize more of the available growing periods may offer a potential

28

approach to restore SOC by maximizing below- and aboveground C inputs. CA has been

practiced since the 1960’s and has spread widely (Friedrich et al., 2012). CA utilizes the basic

tools to create sustainable agriculture based on its three key principles: (i) minimum soil

disturbance (no-till) restricted to sowing rows, (ii) permanent soil cover by organic mulch, and

(iii) crop species diversification (FAO, 2008). This set of improved management practices aims

to enhance soil quality, restore SOC and increase crop productivity (Díaz-Zorita et al., 1999;

Farooq et al., 2011; Govaerts et al., 2009; Sá et al., 2014). SOC accumulation under NT responds

to soil properties (Batlle-Bayer et al., 2010) and the overall amount, quality and frequency of

crop biomass inputs to soils (Batlle-Bayer et al., 2010; Ogle et al., 2012; Virto et al., 2012).

Some physical fractions of SOM are more sensitive to soil management and can be good

indicators of soil management changes over a short-time period (Dou, Wright, & Hons, 2008).

Physical fractionation is a useful tool to interpret SOC dynamics by providing a rough

differentiation between active, intermediate and passive SOC pools. Physical fractionation may

also be used to assess the impact of soil management on dynamics (Cambardella & Elliott, 1994;

Christensen, 1992; Six et al., 1999) and quantitative changes (Bayer et al., 2000) in SOC.

Particle-size fractionation of soil plays an important role in assessing the SOM accessibility

(Gregorich et al., 2006) and interactions between organic and inorganic soil components in the

turnover of SOM (Christensen, 1992, 2001). Particulate organic C (POC), a labile fraction, is a

sensitive pool of organic C and therefore considered a good qualitative indicator with which to

detect changes in SOM due to land use and management (Cambardella & Elliott, 1992; Freixo et

al., 2002). Changes in POC are directly related to the quantity, quality and frequency of crop

residues added to soil (Diekow et al., 2005; Lienhard et al., 2013; Sá et al., 2001; Vieira et al.,

2007). Mineral-associated organic C (MAOC) is considered a stable fraction and is less sensitive

29

than POC to land use and management. It reflects the relationship between SOC and the silt- and

clay-size fractions (Bayer et al., 2001; Sá et al., 2001). MAOC can be changed by physical and

chemical soil environment rather than by land use changes (Guggenberger et al., 1994), resulting

in a lower turnover rate (Feller & Beare, 1997). The results reported by Tivet, Sá, Lal,

Borszowskei, et al. (2013) indicate that the conversion of native vegetation to cultivated land

under CT reduced POC and MAOC pools, with estimated losses of 71% and 40%, respectively,

at 0-5 cm soil depth in a tropical red Latosol.

In Cambodia, some studies on SOC dynamics have been conducted in the forest soils

(Khun, Lee, Hyun, Park, & Combalicer, 2012; Kiyono et al., 2010; Sasaki, 2006; Toriyama et

al., 2012; Toriyama et al., 2011), but there is still a paucity of information on the effects of soil

management practices on SOC dynamics in cropland soils. Although it seems obvious that long-

term CA can be an effective agricultural practice for increasing SOC, its short-term impacts on

SOC dynamics are often variable and not well-documented. The hypothesis of this study was

based on the idea that high and diversified biomass-C inputs in CA might be the first step toward

increasing SOC in the topsoil by creating the C flow to support C storage. Therefore, this study

was carried out to assess the short-term (i.e., five year) responses of SOC, STN, POC and

MAOC fractions in a Cambodian Oxisol to tillage and cropping systems with diverse biomass-C

inputs under NT management.

3.2 Materials and Methods

3.2.1 Site description. The experimental site was located in Chamkar Leu District,

Kampong Cham Province, Cambodia (latitude 12°12′30″N, longitude 105°19′7″E, 118 m

elevation; see Figure 3.1). In 1937, the natural forest at this location was converted to

agricultural land, and crops (including cashew, coffee, mango, mulberry, avocado and rubber)

30

were planted soon after forest clearance. Between 1970 and 1982, the area was abandoned, and

Tetrameles nudiflora, Nauclea officinalis, Cassia siamea and Leucaena glauca grew naturally.

Cotton (Gossypium hirsutum L.) and banana (Musa spp.) were widely planted from 1982 to

2000. From 2000 to 2009, two crops per year, including cotton, mung bean (Vigna radiata (L.)

R. Wilczek), maize (Zea mays L.), sesame (Sesamum indicum L.) and soybean (Glycine max (L.)

Merr.) were rotated under CT before the start of this experiment. Mineral fertilizers such NPK

15-15-15 fertilizer, ammonium phosphate (16-20-0) and potassium chloride (0-0-60) were

applied without lime application (see Figure 3.2b). The soil of the study site is a red Latosol

(equivalent to Oxisols in USDA-Soil Taxonomy or Ferralsols in FAO-Soil Classification)

(Crocker, 1962; Kubota, 2005). Due to forest conversion to rubber plantation in the 1960s in the

areas surrounding the experimental plots, soil samples could not be obtained from the native

forest and vegetation as a reference site. An adjacent reference vegetation (RV) site (latitude

12°12′13″N, longitude 105°19′11″E and 118 m asl) located approximately 500 m from the

experimental plots was selected as a baseline to assess the management-induced changes in SOC

and its fractions in this study. The vegetation composition of RV was an old coffee plantation

under the shade of Leucaena glauca that was planted in 1990. The crop history here was the

same as that of the experimental plots from 1937 to 1990 after conversion of natural forest to

cultivated land (Figure 3.2a). The research site has a tropical monsoon climate with two distinct

seasons, rainy (May-October) and dry (November-April). The mean annual temperature was 28

ºC and the mean annual maximum and minimum temperatures were 32 ºC and 24 ºC,

respectively. The mean annual precipitation (2009–2013) in the experimental site was 1716 mm

distributed mainly over the six months of the rainy season.

31

3.2.2 Experimental design and treatment description. The experiments were initiated

in 2009 by the Conservation Agriculture Service Centre (CASC), General Directorate of

Agriculture of Cambodia in collaboration with Centre de Coopération Internationale en

Recherche Agronomique pour le Développement (CIRAD), France. Three experiments were

conducted as part of this study, including (a) rice-, (b) soybean-, and (c) cassava-based cropping

systems (RcCS, SbCS and CsCS, respectively). The experimental plots of each cropping system

were laid out in a randomized complete block design with three replicates. Plot dimensions were

8 m × 37.5 m. Each cropping system was comprised of four treatments: (a) conventional tillage

(CT) system with disc plowing to a 15- to 20-cm depth, in which the main crops (i.e., rice and

soybean) were planted in annual succession for rice and soybean (i.e., mung bean/rice, –CT-Rc,

sesame/soybean, –CT-Sb) and mono-cropped for cassava (–CT-Cs); (b) NT systems in which the

main crops (rice, soybean, and cassava) were grown in a one-year frequency pattern (NT1-Rc,

NT1-Sb, NT1-Cs); and (c) and (d) NT systems in which the main crops were grown in bi-annual

rotations with maize; the two plots in these bi-annual rotations were NT2-Rc, NT3-Rc for rice,

NT2-Sb, NT3-Sb for soybean and NT2-Cs, NT3-Cs for cassava. The details of main and cover

crop successions are presented in Table 3.1. In NT1, NT2 and NT3, stylo (Stylosanthes

guianensis) was used as a cover crop and grown in association with the main crops. This cover

crop was sown in the middle of the inter-row at 0, 15, and 35 days after the sowing of maize,

cassava, and rice, respectively, and by seed broadcasting at the beginning of soybean maturation,

approximately 30 days before harvest. Congo grass (Brachiaria ruziziensis) was used once in

2009 under NT1-Sb and NT3-Sb (Table 3.1). In addition, if the development and/or density of

the cover crops sown the previous year were considered insufficient, millet (Pennisetum

typhoides) or sorghum (Sorghum bicolor) was sown alone or in alternate lines with sunhemp

32

(Crotalaria juncea) at the beginning of the rainy season. The cover crops were then grown for 60

to 75 days to strengthen the biomass inputs prior to the main cycle of rice, soybean or maize. CT

was operated prior to each crop with a 7-disc plow pulled by an 80-horse-power tractor. Main

and cover crops (at the beginning of the rainy season) were sown with a 2-rows no-till planter

(Fitarelli) drawn by a 12-horse-power hand-tractor. Sesame, mung bean and associated cover

crops were sown manually. Fertilizers were applied under the form of basal application with

thermo phosphate (i.e., 16% P2O5, 31% CaO and 16% MgO), and fractioned top dressing on

main crops with nitrogen and potassium, using urea (46 % N) and potassium chloride (60 %

K2O), respectively, as described in Table 3.2.

3.2.3 Total dry biomass and above- and below ground C inputs. Five sub-plots (10 m

× 2.4 m for rice, soybean and maize; 2.5 m ×1.6 m for sunhemp, millet, stylo, sorghum and

Congo grass; 2 m × 2 m for mung bean and sesame; 1 m × 2 m for cassava leaves) and three sub-

plots (4 m × 5 m for cassava stems) were collected on each plot to measure the aboveground

biomass input. Fresh residues were weighed and 2 kilograms of crop residues were then chopped

and dried at 70 ºC to a constant weight. The moisture content was calculated and the total dry

biomass was converted based on the moisture content of each crop. The belowground biomass-C

inputs from crop residues were estimated by multiplying the root to shoot (RS) ratio by the

aboveground biomass of each crop (Sá et al., 2001; Sá et al., 2013; Sá et al., 2014). Belowground

biomass of cassava was not estimated. The RS ratios were 0.25 for rice, 0.24 for maize, 0.27 for

soybean, 0.27 for millet, 0.26 for sunhemp, 0.38 for Congo grass, 0.30 for sorghum, 0.30 for

mung bean, 0.35 for sesame and 0.33 for stylo. The C concentration (g kg-1 of dry matter) in crop

residues was 459 for rice, 455 for maize, 375 for mungbean, 395 for soybean, 385 for sesame,

33

448 for cassava, 428 for millet, 440 for sunhemp, 443 for Congo grass, 444 for sorghum, and 410

for stylo. Details of cumulative and annual C inputs are presented in Table 3.1.

3.2.4 Soil sampling and processing. Soil samples were taken in November 2011 and

2013. Composite soil samples were collected from each treatment at seven depths: 0-5, 5-10, 10-

20, 20-40, 40-60, 60-80, and 80-100 cm. Bulk soil samples were obtained for the 0-5, 5-10 and

10-20 cm depths by digging 20 × 20 cm trenches and for the 20-40, 40-60, 60-80 and 80-100 cm

depths with an auger (4.5-cm diameter). Soil samples collected from six randomly selected

points within each plot were composited. Bulk soil samples were oven-dried at 40 ºC, gently

ground, sieved through a 2-mm sieve and homogenized. Visible pieces of organic materials were

removed. Similarly, six subplots were demarcated for soil sampling in an approximately 17 ha

area in the adjacent reference vegetation (RV) in 2011 which were used as a baseline for

comparison with the three cropping systems. Bulk soil samples were collected randomly from six

different points at each depth per subplot and composited. In 2011, soil bulk density (ρb) for each

depth was sampled by opening two pits (70 cm × 70 cm) per experimental plot and assessed by

the core method (Blake & Hartge, 1986) using cores of 5 cm in diameter and 5 cm high. A soil

core was obtained in the middle of each of the following depths: 10-20, 20-40, 40-60, 60-80 and

80-100 cm. Two cores were collected for each depth per pit, and soil cores were oven-dried at

105 ºC. Because the soil was heavy-clay, it was assumed that the bulk density had not changed

within two years. Thus, the bulk density was measured only in 2011 and also used to calculate C,

N and POC stocks in 2013.

3.2.5 Soil analysis.

3.2.5.1 Soil chemical and mineralogical properties and particle-size distribution

analyses. The analysis of soil properties was conducted with soil samples collected in 2011 after

34

the third year of the experiment. Soil pH was determined at soil:CaCl2 ratio 1:2.5, and

exchangeable Al3+, Ca2+, Mg2+ were extracted with 1 mol L-1 KCl and K+ with Mehlich-1

solution. Al3+ was determined by titration with 0.025 mol L-1 NaOH, Ca2+ and Mg2+ were

determined by titration with 0.025 mol L-1 EDTA. K+ was determined by flame photometry. All

soil fertility attributes were performed following the procedures described by Pavan, Bloch,

Zempulski, Miyazawa, and Zocoler (1992). Soil samples passed through 20 µm from the RV and

experimental sites at depths of 0-20, 20-40 and 60-100 cm were used to identify clay minerals by

X-ray diffraction technique (Jackson, 1966) using Ultima IV X-ray Diffractometer (RIGAKU,

Japan). The X-ray diffractogram pointed out that the major dominant clay mineral in the soils at

both sites was kaolinite. Particle-size distributions for all depths were determined by a modified

version of the standard Bouyoucos hydrometer method without removal of carbonates and

organic matter (Gee & Bauder, 1986). The results of soil attributes are shown in Table 3.3.

3.2.5.2 Total soil organic C and N concentrations in bulk soils and stock calculation.

Sub-samples of 2-mm sieved bulk soils were finely ground (<150 µm), and then analyzed for

total C and N concentrations by the dry combustion method using an elemental CN analyzer

(TruSpec CN, LECO, St. Joseph, USA). The SOC stocks were calculated using the expression:

SOC stock = (TOC × ρb × th)/10, in which SOC stock is the stock of total organic C at a specific

depth (Mg ha-1), TOC is the concentration of total organic C (g kg-1), ρb is the bulk density (Mg

m-3), and th is the thickness of each soil depth (cm). Due to the significant differences in bulk

density between RV and treated soils (presented in Table 3.4), SOC stocks were calculated for

all depths and computed on an equivalent soil mass-depth basis as described by Ellert and

Bettany (1995).

35

3.2.5.3 Particle-size fractionation of soil organic C. SOC was physically fractionated

using the bulk samples. The particle-size fractionation was performed using a method adapted

from Sá et al. (2001). Briefly, a 40 g soil sample was dispersed with a solution of 1.25 g sodium

hexametaphosphate and 100 mL deionized water and stored for 16 h at approximately 10 ºC.

Then, the sample was horizontally shaken at 100 rpm with three 10-mm diameter agate balls for

8 h. The soil suspension was wet-sieved through a 53-µm sieve with deionized to obtain the

fraction between 53 µm and 2000 µm in size, which represented particulate organic C (POC).

The ≤ 53 µm fraction was transferred to a 1-L glass cylinder and flocculated with 2-g CaCl2.

After complete sedimentation, the supernatant was siphoned. This ≤ 53 µm fraction represents

mineral-associated organic C (MAOC). The two fractions were oven-dried at 40-ºC and finely

ground, and total C was determined using an elemental CN analyzer (as describe above). The

POC and MAOC stocks were computed on an equivalent soil mass-depth basis.

3.2.6 Statistical analysis. Statistical analysis of all data was performed using SAS 9.2

statistical software. To compare the effects of tillage and crop rotation treatments at each depth

in each cropping system, data were subjected to analysis of variance procedures with randomized

complete block design. Comparisons among treatment means were calculated based on least

significant difference (LSD) tests at the 0.05 probability level, unless otherwise stated.

3.3 Results

3.3.1 Soil organic C (SOC) and soil total N (STN).

3.3.1.1 Rice-based cropping systems. No significant increase in SOC concentrations

under the three NT treatments were observed when compared to CT-Rc in all soil depths (Figure

3.3). The SOC concentrations under CT-Rc and NT-Rc (average of NT1-Rc, NT2-Rc and NT3-

Rc) were 19.68 g kg-1 and 19.15 g kg-1, respectively, at the 0-5 cm depth in 2011. Although

36

higher SOC concentration was found in CT-Rc soil at 0-5 cm depth in 2011, NT-Rc soils

accumulated an average of 10% more SOC in 2013 compared with CT-Rc. At deeper soil layers,

there were no noticeable differences between CT-Rc and NT-Rc soils. It was observed that SOC

concentrations decreased with increasing soil depth. Similar to SOC, results of STN

concentrations showed no significant differences between CT-Rc and three NT-Rc treatments

(Figure 3.3). NT-Rc soils had a higher STN concentration than that of CT-Rc, ranging from 3%

to 8% in 2011, and 6% to 10% 2013 at 0-5 cm depth. The bi-annual crop rotation treatments

(NT2-Rc and NT3-Rc) also showed an increased accumulation of STN, with 7% in 2011 and

10% in 2013 at 5-10 cm depth.

Differences in tillage and crop rotation treatments did not show a significant effect on

SOC and STN stocks at any depth (Table 3.5 & 3.6). NT-Rc soils (average of NT1-Rc, NT2-Rc

and NT3-Rc) stored 3% less SOC stock than that of CT-Rc soil at 0-5 cm depth in 2011.

However, NT-Rc had 10% more SOC stock than that of CT-Rc in the surface layer in 2013. NT-

Rc showed an increase in 0.8 Mg ha-1 for SOC, and 0.05 Mg ha-1 for STN at 0-5 cm depth in

2011 compared with the initial stocks in 2009. SOC and STN stocks under RV were significantly

higher than those under CT-Rc and NT-Rc at 0-5 and 5-10 cm depths (P<0.001 and P<0.01,

respectively). In 2011, SOC stock under RV was 58% and 63% significantly greater than those

under CT-Rc and NT-Rc, respectively, at 0-5 cm. However, NT-Rc soils tended to sequester

more SOC compared with that of CT-Rc in 2013, when the percentage of SOC stock under RV

was 46% and 33% greater than those under CT-Rc and NT-Rc soils, respectively. Considering

the 100 cm as a single stratum, no differences were found among treatments for SOC reserves in

either 2011 or 2013, or for STN reserves in 2011. The changes in sequestration rates of NT-Rc

treatments were twice as high (average rate of 5.77 Mg C ha-1 yr-1) as that of CT-Rc soil (2.95

37

Mg C ha-1 yr-1). In contrast, STN reserves decreased in all treatments by rates of 0.16 and 0.83

Mg C ha-1 yr-1 under CT-Rc and NT-Rc soils, respectively.

3.3.1.2 Soybean-based cropping systems. SOC concentrations significantly increased

(P<0.05) in the 0-5 cm depth in response to tillage and crop rotation treatments in both 2011 and

2013 (Figure 3.4). The SOC concentrations of soils under NT2-Sb and NT3-Sb (bi-annual crop

rotations) were 8% and 4% greater, respectively, than that under CT-Sb in 2011. CT-Sb soil

showed a 5% increase in SOC concentration in 2013. However, NT-Sb soils contained

significantly more 20% SOC than that of CT-Sb. NT soils accumulated more SOC when

compared with the percentage of SOC sequestered by NT-Sb from 2011 to 2013. SOC

concentrations under NT1-Sb, NT2-Sb and NT3-Sb increased 21%, 13%, and 23%, respectively.

RV soil contained greater SOC concentrations (61% and 53% at 0-5 cm depth and 14% and 23%

at 5-10 cm depth) than CT-Sb and NT-Sb soils, respectively, in 2011. SOC results in 2013

showed an increase in both CT-Sb and NT-Sb soils. However, NT-Sb soils still maintained

higher accumulated SOC than that of CT-Sb (based on their differences from RV soil) as they

decreased from 53% to 28% at 0-5 cm and 23% to 17% at 5-10 cm, whereas these values under

CT-Sb decreased from 61% to 53% at 0-5 cm and 14.4% to 14.2% at 5-10 cm. STN sampled in

2011 did not differ among tillage and crop rotation treatments, but effects were detected in 2013

at 0-5, 5-10, 20-40 and 60-80 cm depths (Figure 3.4). STN concentrations were greater in NT-Sb

soils, particularly NT2-Sb and NT3-Sb. RV soil contained 74% and 66% at 0-5 cm and 35% and

30% at 5-10 cm STN concentrations that were higher than those of CT-Sb and NT-Sb soils,

respectively, in 2011. When comparing changes from 2011 to 2013, STN concentrations

decreased 12% and 18% under CT-Sb but increased 5% and 3% under NT-Sb at 0-5 and 5-10 cm

depths, respectively.

38

SOC stocks were higher by 6% in 2011 and 20% in 2013 under bi-annual rotation

treatments (NT2-Sb and NT3-Sb) when compared with those of CT-Sb (Table 3.7). SOC stock

under NT1-Sb did not differ from that of CT-Sb in 2011, but a significant difference was

detected in 2013, in which NT1-Sb stored 20% greater SOC stock. An increase of SOC in

subsoil layers was observed in both NT-Sb and CT-Sb. SOC stocks under RV soils were

significantly higher than those under CT-Sb and NT-Sb at 0-5 cm and 5-10 cm depths in 2011

and 2013. The SOC stock under NT-Sb (average of NT1-Sb, NT2-Sb and NT3-Sb) at 0-5 cm

depth was 53% lower than that under RV in 2011. It decreased to 28% in 2013 and decreased

from 61% to 53% in CT-Sb soil. When comparing with RV, it was evident that SOC stocks

decreased in the order RV > NT > CT only at 0-5 cm soil depth in 2011 and 2013. From 2011 to

2013, changes in the SOC reserves at 100 cm depth (as a single stratum) indicated an increasing

trend of NT-Sb over CT-Sb. Soils under NT1-Sb, NT2-Sb and NT3-Sb sequestered 1.75, 2.45,

and 2.85 Mg C ha-1 yr-1, respectively. STN stocks were not affected by tillage and crop rotation

treatments in 2011 but significant differences between CT-Sb and NT-Sb were observed at 0-5

and 5-10 cm depths (P<0.01 and P<0.05, respectively) in 2013 (Table 3.8). In 2013, an increase

of 0.21 Mg N ha-1 was recorded in NT-Sb soils at 0-5 cm and 5-10 cm depths. Considering the

100 cm depth to be as a single stratum, average STN stocks under NT2-Sb and NT3-Sb stored

55% greater than under CT-Sb. Although 29% more STN stock was found in NT1-Sb, this

treatment did not significantly differ from CT-Sb.

3.3.1.3 Cassava-based cropping systems. Similar to SbCS, tillage and crop rotation

treatments affected the SOC concentrations only at the 0-5 cm layer (P<0.05) in both 2011 and

2013. Significant changes (P<0.01) in STN concentrations were observed at 0-5 and 5-10 cm

depths in 2013 (Figure 3.5). On average, the bi-annual crop rotation treatments (NT2-Cs and

39

NT3-Cs) experienced an increase in SOC concentration of 17% in 2011 and 22% in 2013 over

that of CT-Cs. NT1-Cs did not differ in SOC concentration when compared with CT-Cs.

Although they were not significantly different, NT1-Cs accumulated 11% more SOC in 2013.

Similarly, STN concentration in NT3-Cs was 21% higher than that of CT-Cs in 2011 and 31%

higher in 2013 at 0-5 cm depth (Figure 3.5). STN concentrations under the other two NT

treatments (NT1-Cs and NT2-Cs) were higher but not significantly different from those under

CT-Cs at 0-5 cm depth in 2013.

Differences in tillage and crop rotations resulted in significant differences in SOC stocks

at 0-5 cm depth in 2011 (P<0.05) and 2013 (P<0.01) (Table 3.9), and STN stocks (P<0.01) at 0-5

and 5-10 cm depths in 2013 (Table 3.10). Soils under NT2-Cs and NT3-Cs stored 15% and 19%

higher SOC stocks in 2011 and 16% and 28% in 2013, respectively, than under CT-Cs at 0-5 cm

depth. In 2013, SOC stock was greater under NT1-Cs than under CT-Cs, but not significantly so.

SOC stocks under CT-Cs, NT1-Cs, NT2-Cs and NT3-Cs were 99%, 99%, 72% and 67% lower,

respectively, than under RV at 0-5 cm depth in 2011. SOC stocks increased in 2013 and the

differences with RV dropped to 78%, 60%, 53% and 40% under CT-Cs, NT1-Cs, NT2-Cs and

NT3-Cs, respectively. Changes in the SOC reserves at 0-100 cm depth from 2011 to 2013

indicated that NT-Cs produced greater values than CT-Cs; soils under NT1-Cs, NT2-Cs and

NT3-Cs sequestered 2.80, 2.35 and 3.30 Mg C ha-1 yr-1, respectively, more than CT-Cs soil.

There were no noticeable changes in STN stocks at any depth from 2011 to 2013. However,

NT2-Cs and NT3-Cs stored 13% and 31% significantly greater STN stocks, respectively, when

compared with CT-Cs at 0-5 cm depth as well as 23% greater under NT3-Cs at 5-10 cm in 2013.

40

3.3.2 Particulate and mineral-associated organic C (POC and MAOC).

3.3.2.1 Rice-based cropping systems. The adoption of NT crop rotations significantly (P

< 0.05) increased POC concentrations at 0-5 cm depth in 2013 (Figure 3.6b). Soil under NT3-Rc

accumulated 35% greater POC than under CT-Rc. NT1-Rc and NT2-Rc did not differ from CT-

Rc but an increasing trend of 16% and 15% more POC than CT-Rc under NT1-Rc and NT2-Rc,

respectively, was observed. No significant differences in POC and MAOC concentrations were

observed in 2011 at any depth except 80–100 cm for MAOC (Figure 3.6a). The POC

concentrations in the two highest soil layers noticeably increased in all treatments from 2011 to

2013. This increase was also observed at deeper soil depths. When comparing treatments,

MAOC concentrations were nearly constant at all depths, except soils at80-100 cm at which CT-

Rc and NT2-Rc soils contained the highest MAOC concentrations. Silt plus clay-associated C,

averaged across all soil depths, represented 88%, 84%, 92%, 89% and 86% of TOC under RV,

CT-Rc, NT1-Rc, NT2-Rc and NT3-Rc, respectively.

POC stocks and MAOC stock in 2011 were not influenced by treatments, except for

MAOC stock at 80-100 cm depth. However, POC stocks under NT-Rc significantly (P<0.05)

increased at 0-5 cm depth compared with those of CT-Rc in 2013 (Tables 3.11 and 3.12).

Compared with POC stocks in 2011, the increased rates in 2013 ranged from 0.43 to 0.68 Mg ha-

1 at 0-5 cm depth. When comparing with RV, POC stocks under treated soils were significantly

lower at 0-5, 20-40 and 40-60 cm depths in 2011 but differed only at 0-5 cm depth in 2013.

Similarly, significantly greater MAOC stocks under RV than those under CT-Rc and NT-Rc

were detected at 0-5 and 5-10 cm depths in 2011. RV soil on average had 37% and 35% higher

MAOC stocks at 0-5 cm and 22% and 23% higher MAOC stocks at 5-10 cm than CT-Rc and

NT-Rc soils, respectively. Considering the 100 cm as a single stratum, RV soil had greater POC

41

and MAOC stocks than the treated soils in 2011 but greater POC stocks under RV were not

apparent in 2013. The POC stocks at 0-20 cm depth were 66% (for RV), 71% (CT-Rc, NT1-Rc

and NT2-Rc), and 72% (NT3-Rc) of the total POC stocks in 0-100 cm.

3.3.2.2 Soybean-based cropping systems. Tillage and crop rotation treatments did not

significantly affect POC concentrations in any soil layers in either 2011 or 2013, with the

exception of POC at 40-60 cm and MAOC at 0-5 cm and 40-60 cm depths in 2011 (Figure 3.7).

Although they did not differ in the surface layer, NT treatments on average had 20% greater

POC concentration than that of CT-Sb at 0-5 cm depth in 2013. POC concentrations in all

treatments increased in 2013 compared with those in 2011 and POC concentration was greater

(though not significantly so) NT-Sb compared with CT-Sb. MAOC concentrations were greater

in all NT-Sb treatments in the soil surface. Soils under NT1-Sb, NT2-Sb and NT3-Sb had 6%,

12% and 8% higher MAOC concentrations, respectively, than those under CT-Sb. Similar to

RcCS, silt plus clay-associated C was the dominant proportion of the fractions. Averaged across

all soil depths, it represented 87%, 90%, 90% and 89% of SOC concentrations under CT-Sb,

NT1-Sb, NT2-Sb, and NT3-Sb, respectively.

Significant effects of tillage and crop rotations on POC stocks were not detected, with the

exception of those at 40-60 and 80-100 cm depths (Table 3.13). Greater MAOC stocks in soils

under NT-Sb compared with those under CT-Sb occurred at 0-5 and 40-60 cm depths in 2011

(Table 3.14). POC stocks under NT-Sb soils tended to be higher (but not significantly so) than

under CT-Sb soils. In 2013, CT-Sb, NT1-Sb, NT2-Sb and NT3-Sb practices increased POC

stocks by 65%, 100%, 70%, and 73%, respectively, when compared with POC stocks in 2011 at

0-5 cm depth. A slight increase was also observed in the subsoil layers. However, the major POC

stocks were found in the 0-20 cm depth where they represented 68% (CT-Sb), 65% (NT1-Sb),

42

70% (NT2-Sb) and 71% (NT3-Sb). In 2011, RV soils contained 146% and 123% significantly

higher POC stocks at 0-5 cm depth, and 56% and 70% significantly POC stocks at 5-10 cm depth

than those at CT-Sb and NT-Sb soils, respectively. However, these values did not differ in 2013.

Considering the 100 cm as a single stratum, POC stocks under NT-Sb increased (but not

significantly so) when compared with those under CT-Sb. From 2011 to 2013, NT1-Sb, NT2-Sb,

and NT3-Sb were greater by 0.16, 0.35, and 0.33 Mg C ha-1, respectively, than that of CT-Sb.

When compared with RV, POC stocks of treated soils were significantly lower at most depths in

2011 but not significantly different in 2013. In contrast to POC stocks, soils under NT1-Sb, NT2-

Sb and NT3-Sb had 6%, 12% and 8% significantly higher MAOC stocks, respectively, than soils

under CT-Sb at 0-5 cm depth in 2011. When compared with RV, MAOC stocks under RV were

55% and 43% significantly higher than that of CT-Sb and NT-Sb soils at 0-5 cm. The

significantly lower stock was also detected at 5-10 cm (Table 3.14). MAOC accounted for 80%

of SOC stock under cultivated fields (CT-Sb and NT-Sb) and 86% under RV (100 cm considered

a single stratum).

3.3.2.3 Cassava-based cropping systems. POC concentrations at 0-5 cm depth were

influenced by tillage and crop rotation treatments after five years (P < 0.01). Although they did

not significantly differ in 2011, bi-annual rotation treatments (NT2-Cs and NT3-Cs) tended to be

greater than CT-Cs. (Figure 3.8a). From 2011 to 2013, POC concentrations increased in all

treatments, but the greatest increase was found in the 0-20 cm depth. In contrast, MAOC

concentrations were not affected by the treatments (Figure 3.8b). The proportion of MAOC

remained constant. The silt plus clay-associated C, averaged across all soil depths, represented

89% of the SOC concentrations in cultivated fields (CT-Cs and NT-Cs).

43

Significant differences in POC and MAOC stocks were not detected in 2011, but the

adoption of NT significantly (P < 0.01) increased POC stocks at the 0-5 cm depth in 2013

(Tables 3.15 and 3.16). NT2-Cs and NT3-Cs had 56% and 127% greater POC stocks,

respectively, than that of CT-Cs. After five years under the same NT systems, NT2-Cs and NT3-

Cs were more likely to have increased POC stocks compared with NT1-Cs after five years. The

POC stocks in the 0-10 cm depth represented 1.2, 1.3 and 1.6 times more C under NT1-Cs, NT2-

Cs, and NT3-Cs, respectively, than under CT-Cs after five years of NT practices. Although

MAOC stocks did not differ among treatments, bi-annual rotations under NT systems (NT2-Cs

and NT3-Cs) on average had a 7% increase in MAOC stocks than under CT-Cs. RV soil had the

highest POC stock at 0-5 cm depth in both 2011 and 2013. However, NT systems tended to

restore POC stocks nearly to the level under NT (particularly NT3-Cs). On average, MAOC

stocks under RV were greater than those under CT-Cs and NT-Cs by 75% and 67% at 0-5 cm

and 30% and 32% at 5-10 cm, respectively. Considering 100 cm as a single stratum, no

significant differences in MAOC stocks between RV and cultivated fields (CT-Cs and NT-Cs)

were apparent in 2011.

3.4 Discussion

3.4.1 Changes in Soil organic C and soil total N. Short-term (≤ 10 years) effects of

agricultural management practices on SOC vary with soil conditions, climate, biomass-C return

and the management itself (Al-Kaisi, Yin, & Licht, 2005). NT cropping system practices result in

SOC increase in tropical soils compared with CT systems (Bayer, Martin-Neto, et al., 2006; Neto

et al., 2010). The types of crop rotations and NT management practices produce significant

changes in SOC sequestration due to an increase in biomass-C inputs returned to the soil and a

decrease in soil disturbance. Sá et al. (2013) reported that NT cropping rotations with high C

44

input cover crops maintain a permanent soil cover and support a continuous flow of biomass that

releases organic compounds. However, the rate of SOC in short-term NT cropping systems with

cover crops might be detected in the surface soil layer. In the present study, the bi-annual crop

rotation treatments in SbCS and CsCS (NT2-Sb, NT3-Sb, NT2-Cs and NT3-Cs) increased in the

surface soil layer after five years. In a tropical Oxisol in Laos, Lienhard et al. (2013) found an

increase in SOC of approximately 15% at a soil depth of 0-10 cm under NT cropping system

practices associated with diverse cover crops when compared with CT after two years. Over a

five-year period in a semi-arid tropical climate, Scopel et al. (2005) found that soil C levels in

mulch increased by 23 to 29% when compared with those of CT, mainly due to increased crop

residue inputs and reduced soil C erosion in mulch treatments. Sá et al. (2013) also observed a

significant change in SOC over eight-year NT cropping systems in association with Congo grass,

sorghum and millet in a Brazilian Oxisol. This type of short-term effect was reported by

McCarty, Lyssenko, and Starr (1998) in a temperate climate. They found a substantial increase

of SOC (38%) in the 0-2.5 cm soil layer in NT soil after the first three years of tillage transition

from plow tillage to NT. This increase in SOC in the surface soil could be related to the fact that

in NT cropping systems with cover crops, soil was undisturbed and higher biomass-C was added,

which create a positive C budget and accentuated C transformation and flow (Boddey et al.,

2010; Sá et al., 2013). This finding also supports the hypothesis that a greater SOC accumulation

over the short-term in NT cropping systems is found only in the top soil, when compared with

that of CT. SOC accumulation in the soil surface is essential for identification of C restoration in

response to biomass-C inputs and absence of physical disruption. The longer-term NT effects on

SOC accumulation are apparent, but, empirical evidence in deeper layers of the soil profile is

still scant due to the continuous biomass-C inputs. Sá et al. (2013) found a strong linear

45

relationship between annual C input and annual SOC sequestration to soil depth of 1-m when

deeply rooted cover and main crops were planted under eight years in NT systems in an Oxisol

from humid tropic environment.

SOC sequestration is controlled by variability in the quantity and quality of biomass-C

inputs (Ogle et al., 2005) and is increased with higher crop residue inputs and cropping intensity

(Franzluebbers, Hons, & Zuberer, 1998). The soil from annual frequency pattern of soybeans

(NT1-Sb) with various cover crops such as Congo grass, millet, stylo and sorghum have an

increase in SOC after five years when compared with CT soil, but not NT1-Cs. The possible

explanation could be that cassava was associated only with stylo, resulting in lower biomass-C

inputs than other NT cropping sequences in CsCS. The higher N input obtained from stylo

biomass under NT1-Cs than from that under CT-Cs could be associated with easily

decomposable residues of cassava that result in more C oxidation than C converted to SOC. The

SOC increase under NT cropping systems with diverse biomass-C inputs in RcCS did not lead to

a significant difference from CT soil in the topsoil after five years. However, an increasing trend

of SOC under NT soils over CT was observed. Nascente et al. (2013) revealed a similar increase

in SOC at the 0-5 cm soil layer between NT and CT soils after two-year of NT rice cropping

with cover crops in a tropical savanna climate. This report supports the occurrence of a starting

point that stimulates the C restoration process. Zotarelli et al. (2007) emphasized that short-term

changes in total SOC as a result of soil management practices are often difficult to detect. It was

somewhat unexpected that NT, in combination with high crop residues returned to the soil, did

not have a beneficial impact on SOC when compared with CT during this period, whereas SbCS

and CsCS did have a beneficial impact on SOC. One explanation could be that biomass-C inputs

retained in the NT soil surface over the experimental period were not adequate to significantly

46

increase SOC when compared with those of CT soil. The annual biomass-C inputs from rice

residues under CT-Rc (2.84 Mg ha-1) were 30% and 76% higher than those of CT-Sb and CT-Cs,

respectively. The biomass-C inputs from rice residues might contain higher lignin and lower N

contents than soybean residues, leading to a lower SOC mineralization rate. The presence of

legumes such as Crotalaria sp. can provide enough N to support the conversion of C from

grasses to SOC (Boddey et al., 2010). In fine textured soils, clay- and silt-sized particles with

high surface activities may chemically stabilize SOC and form the building blocks for aggregates

that lead to the establishment of SOC physical protection (Six et al., 1999). With time, SOC

under NT soils in RcCS might surpass that under CT soils because a higher trend was evident

after five years in the present study.

CT practices involving the removal of crop residues can lead to a reduction in SOM due

to accelerated decomposition and loss of topsoil that is rich in organic matter (Arshad, Schnitzer,

Angers, & Ripmeester, 1990). Addition of crop residues to the soil is important because crop

residues are a major source of C and N, which can replenish SOC and STN (Al-Kaisi et al.,

2005). In the present study, CT soil still received the annual biomass-C inputs from crop residues

which were maintained and spread in the soil surface resulting in a slight increase in SOC in the

three cropping systems from 2011 to 2013. However, NT practices consistently outperformed the

potential to sequester more SOC as a result of greater biomass-C inputs.

SOC stored at deeper depths may be in more stable forms (Angers & Eriksen-Hamel,

2008). SOC levels in the soil profile can be enhanced by the change in vegetation to deep-rooting

crops that significantly affect the vertical distribution of SOC deep in the soil profile, acting as a

potential C sink (Jobbágy & Jackson, 2000). The present study shows that NT with high

biomass-C inputs potentially increases SOC in the top soil layer and most likely in the deep

47

layers in the three cropping systems. This increase may be due to the rotation of main crops (i.e.,

rice, soybean, maize and cassava) with deep-rooted cover crops such as millet, sorghum, Congo

grass and sunhemp that provide greater biomass-C inputs via roots. Séguy et al. (2006) reported

that SOC in the subsoil could be sequestered by higher SOC rhizodeposition of deep rooting

systems such as Congo grass, sorghum and Crotalaria sp. However, the subsoil consistently

accumulated less SOC under than under CT in the three cropping systems. This finding was

probably because the incorporation of forage species into crop rotations provides more root

biomass inputs in the deep soil layers and seems to increase microbial activities (Lienhard et al.,

2013). During the dry season, when no crops were planted in CT plots, SOC in NT soils could be

degraded due to fresh C inputs in the subsoil from root exudates. Fresh C inputs cause an

increase in SOC decomposition by microbes, which are also able to decompose the recalcitrant C

compounds with their enzymes by using fresh C as a source of energy (Fontaine et al., 2007).

Additionally, the incorporation of residues in the soil through disc plowing might result in

greater deep soil SOC than under NT. This difference may be due to the slower decomposition of

buried residues when compared with the residues left at the soil surface under NT, which may be

susceptible to decomposition. Shan, Yang, Yan, and Wang (2005) reported that frequent tillage

may accelerate the movement of SOM to deep soil layers. Thus, the results suggest that soils that

have undergone NT for five years in this tropical agro-ecosystem have higher SOC in the surface

layer than CT soils. However, SOC levels at lower depths are similar in both tillage systems or

slightly higher under CT when sampling was extended to 100 cm depth. When compared with

RV soil, SOC decreased in the order RV > NT > CT at only the 0-5 cm depth. This finding

suggest that there is a greater potential for NT practices in the three cropping systems to restore

SOC previously depleted by land conversion than there is for CT practices, due to the amount of

48

biomass-C inputs via crop residues returned to the soil that could increase the SOC level. Tivet,

Sá, Lal, Borszowskei, et al. (2013) found the restoration of SOC in tropical soils under NT crop

rotations with cover crops leads to an increase in the resilience of agro-ecosystems.

Similar to SOC, STN in NT soil surface layer (especially that in the bi-annual rotation

treatments in SbCS and CsCS) showed an increasing trend over that of CT. In contrast, the

adoption of NT crop rotations with cover crops did increase STN in RcCS after five years.

However, NT soils tended to accumulate more STN compared with CT soils at the surface layer,

and a significant change might become evident with time. This finding is reflective of the

differing amounts of above- and belowground crop biomass and types of crop residues returned

to the soil. Grass and legume cover crops act as a source of supplemental N in the soil (Wagger,

Cabrera, & Ranells, 1998), and so soil N can be increased by increasing in the amount of residue

returned to the soil (Ghimire, Adhikari, Chen, Shah, & Dahal, 2012). In the present study,

several grass and legume species such as Congo grass, millet, sorghum, stylo and sunhemp were

rotated and/or associated with the main crops under NT systems. Thus, they could play a major

role in providing N to the soil. Figueiredo, Resck, and Carneiro (2010) reported that adding crop

residues added to the soil surface under NT systems led to an increase in STN. When comparing

STN in 2011 and 2013, there were no noticeable changes in the three surface soil layers between

CT and NT soils, with the exception of those under NT3 in the three cropping systems. However,

a decrease in the four deeper layers was observed in most cases. This observation could be

attributed to the fact that the inclusion of legume and grass species in the crop rotations increased

root exudates and released more N in the subsoil. Consequently, N mineralization in the soils

under NT systems surpassed CT soils due to higher microbial activities during the six-month dry

season, as it happened to SOC.

49

3.4.2 Changes in particulate and mineral-associated organic C. Water soluble C

(WSC) is the main energy and substrate source of soil microorganisms and is positively

proportional to soil microbial biomass and activity. On average all depths in each cropping

system (RcCS, SbCS, and CsCS), lost SOC during the physical fractionation process in the

amounts of 7% in RV and NT soils to 13% in CT in 2011, representing greater WSC under RV

and NT systems. Tivet, Sá, Lal, Borszowskei, et al. (2013) reported losses of SOC in bulk soil

during fractionation ranged from 8% to approximately 15% on a clayed tropical Oxisol of the

Brazilian Cerrado.

The decomposition process of crop residues, including the transition from particulate C

fraction to mineral-associated C fraction, results in the stabilization of SOC with time (Bayer et

al., 2001; Briedis, Sá, Caires, de Fátima Navarro, et al., 2012; Sá et al., 2001; Tivet, Sá, Lal,

Borszowskei, et al., 2013). Particulate organic C (POC) is biologically and chemically active and

is a part of the labile pool of SOM. POC is viewed as a good indicator of the quality of soil

management systems (Cambardella & Elliott, 1992). Evaluation of the POC fraction might

appear easy to assess, especially in the topsoil layer, which is primary location of potentially

sequestered POC in short-term NT crop rotations with cover crops (Nascente et al., 2013). In

general, NT practices in the three cropping systems resulted in a greater increase in POC in the

surface layer after five years that that of CT practices. A possible explanation could be associated

with greater biomass-C inputs via various cover crops placed on the surface of NT practices. The

presence of significant differences in POC at 0-5 cm depth was observed in RcCS and CsCS.

The bi-annual crop rotation treatments (NT2 and NT3) were likely to have greater increase in

more POC than that of NT1. Although the adoption of NT crop rotations with cover crops did

not result in a significant increase over CT in SbCS, NT practices tended to have higher POC in

50

the topsoil that that of CT practices. This finding suggests that continuing NT cropping system

practices with high biomass-C inputs from diversified crop species would result in a greater

quantity of POC when compared with that of CT practices. Sá et al. (2001) indicated that there

was an increase in the proportion of SOC concentrations in POC from crop residues added to the

soil under NT (after conversion of CT to NT). The continuous biomass-C inputs from grass and

legume cover crops act as a source of supplemental N to the soil (Wagger et al., 1998) that might

result in a greater decrease of POC under NT than CT. Salvo, Hernández, and Ernst (2010)

reported that N input may favor humification processes in POC. POC noticeably increased in all

treatments in the three cropping systems from 2011 to 2013 but CT experienced the lowest

increase. This increase was also observed in the deeper soil layers; fresh above- and

belowground residue inputs from main deep rooting cover crops in the crop rotations could have

contributed to this change. This finding contradicts other studies, which have shown that POC is

strongly related to the quality and quantity of crop residues added to the soil and soil

management practices (Alvarez, Alvarez, Daniel, Richter, & Blotta, 1998; Diekow et al., 2005;

Vieira et al., 2007). Short-term NT cropping systems, in rotation or association with cover crops,

have a greater potential to restore POC. Compared with soil POC under RV, NT crop rotations

with diversified cover crops offered the potential to restore POC after five years in this study.

Mineral-associated organic C (MAOC) obtains stability from physical sorption to

minerals and subsequently chemical bonds with the surface (Feller & Beare, 1997; Kaiser,

Mikutta, & Guggenberger, 2007). It is highly stable to biological decomposition due to

interaction with variably charged minerals (Bayer, Mielniczuk, Giasson, Martin‐Neto, &

Pavinato, 2006) so MAOC can be protected by its interaction with minerals. The changes in

MAOC could be related to C migration from POC with time and bonding of SOC with soil

51

colloids (Briedis, Sá, Caires, de Fátima Navarro, et al., 2012). In the present study, increased

MAOC in the surface layer in the three cropping systems was consistently related to POC.

Although the constant addition of biomass-C inputs under NT resulted in a MAOC increase,

significant effects were detected only in SbCS. This increase could be related to the transition

from POC to MAOC, which can stabilize SOC with time. The MAOC fraction comprised a

major portion of SOC concentration (77%-96%) when compared with POC fraction. In most

cases, MAOC concentrations increased with increasing depths. These results indicate that the

soils used in this study have a good potential to contain large amounts of SOC due to high

MAOC fractions that physically protect SOC. The presence of oxides and sesquioxides of iron

and aluminum in Oxisols could act as binding agents between mineral particles and humic

substances. Thus, significant effects of short-term CT systems on SOC depletion might be

difficult to detect due to the high stability of clay and silt-sized microaggregates that result from

physical SOC protection within the pores of microaggregates.

3.5 Conclusions

The main impact of short-term CA on SOC was found in the surface soil layer (0-5 cm)

in SbCS and CsCS. Similarly, POC was affected only in the surface soil layer in RcCS and

CsCS. Significant changes in SOC in RcCS and POC in SbCS under NT management practices

might become evident with time, especially under bi-annual crop rotations. An increase in SOC

and POC in soils under CT was still observed in this study and might have been related to the

biomass-C inputs returned to soils after grain harvest of rice, soybean and maize, and root

harvest of cassava (leaf inputs from cassava). The sequestration rates of intensive NT cropping

systems with higher soil additions of biomass-C inputs led to enhanced SOC storage/ this

constitutes an effective way to restore SOC over time. In this study, SOC and its size-fraction

52

results suggest that bi-annual crop rotations are the appropriate crop rotation scheme to

potentially restore SOC in the surface soil layer in a short-term CA and create a continuous flow

in a clayed Cambodian Oxisol. These results also support the promising idea that SOC maybe

vertically distributed in deeper layers in long-term CA in response to high biomass-C inputs from

deep-rooting cover crops.

53

Figure 3.1. Location map of the research site.

Figure 3.2. Chronology of land use in the research site: (a) reference vegetation and (b)

experimental site.

54

Figure 3.3. Soil total N (STN) and soil organic C (SOC) concentrations in soils at 0- to 100-cm

depths under different soil management and crop sequences (RV: reference vegetation; CT-Rc:

conventional tillage; NT-Rc: no-till) in rice-based cropping systems in (a) 2011 and (b) 2013.

Error bars represent the standard error of the mean.

0 5 10 15 20 25 30 35

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

0-5

5-1

01

0-2

02

0-40

40-6

060

-80

80-1

00

SOC and STN concentrations (g kg-1)

Dep

th (

cm)

STN SOC

0 5 10 15 20 25 30 35

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

0-5

5-1

01

0-2

02

0-40

40-6

060

-80

80-1

00


Dep

th (

cm)

STN SOC

(a) (b)

55



conventional tillage; NT-Rc: no-till) in soybean-based cropping systems in (a) 2011 and (b)

2013. Error bars represent the standard error of the mean.

0 5 10 15 20 25 30 35

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

0-5

5-10

10-2

020

-40

40-6

06

0-80

80-

100


Dep

th (

cm)

STN SOC

0 5 10 15 20 25 30 35

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

0-5

5-10

10-2

02

0-40

40-6

060

-80

80-1

00


Dep

th (

cm)

STN SOC

(a) (b)

56



conventional tillage; NT-Rc: no-till) in cassava-based cropping systems in (a) 2011 and (b) 2013.

Error bars represent the standard error of the mean.

0 5 10 15 20 25 30 35

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

0-5

5-10

10-2

020

-40

40-6

060

-80

80-1

00


Dep

th (

cm)

STN SOC

0 5 10 15 20 25 30 35

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

0-5

5-10

10-2

020

-40

40-6

060

-80

80-1

00


Dep

th (

cm)

STN SOC

(a) (b)

57

Figure 3.6. Particulate organic C (POC) and mineral-associated organic C (MAOC)

concentrations in soils at 0- to 100-cm depths under different soil management and crop

sequences (RV: reference vegetation; CT-Rc: conventional tillage; NT-Rc: no-till) in rice-based

cropping systems in (a) 2011 and (b) 2013 (only POC presented). Error bars represent the

standard error of the mean.

0 5 10 15 20 25 30

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

0-5

5-10

10-

20

20-4

04

0-60

60-8

08

0-1

00

POC and MAOC concentrations (g kg-1)

Dep

th (

cm)

POC MAOC

0 1 2 3 4

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

0-5

5-10

10-

20

20-4

04

0-60

60-8

08

0-1

00

POC concentrations (g kg-1)

Dep

th (

cm)

POC

(a) (b)

58



sequences (RV: reference vegetation; CT-Rc: conventional tillage; NT-Rc: no-till) in soybean-

based cropping systems in (a) 2011 and (b) 2013 (only POC presented). Error bars represent the


0 5 10 15 20 25 30

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

0-5

5-1

01

0-2

02

0-40

40-6

060

-80

80-1

00


Dep

th (

cm)

POC MAOC

0 1 2 3 4

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

0-5

5-1

01

0-2

02

0-40

40-6

060

-80

80-1

00


Dep

th (

cm)

POC

(a) (b)

59



sequences (RV: reference vegetation; CT: conventional tillage; NT1-3: no-till) in cassava-based

cropping systems in (a) 2011 and (b) 2013 (only POC presented). Error bars represent the


0 5 10 15 20 25 30

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

0-5

5-1

01

0-2

02

0-40

40-6

060

-80

80-1

00


Dep

th (

cm)

POC MAOC

0 1 2 3 4

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

0-5

5-1

01

0-2

02

0-40

40-6

060

-80

80-1

00


Dep

th (

cm)

POC

(a) (b)

60

Table 3.1

Land use, crop sequence and C input in the five-year experiment period (2009-2013)

C input (Mg ha-1)

Land use Crop sequence Cumulative Annual

Rice-based cropping systems

CT-Rc NT1-Rc NT2-Rc NT3-Rc

Mb/Rc – Mb/Rc – Mb/Rc – Mb/Rc – Mb/Rc Mt/Rc+St – Mt+Cr/Rc+St – St(2010)/Rc+St – St(2011)¥/Rc+St – Mt+St(2012)/Rc+St Mt/Rc+St – Mt+Cr+St (2009)/Mz+St – Mt+Cr+St (2010)/Rc+St – St(2011)/Mz+St – St (2012)/Rc+St Mt/Mz+St – Mt+Cr+St (2009)/Rc+St – St (2010)/Mz+St – St (2011)/Rc+St – St (2012)/Mz+St

14.22 31.75 30.29 33.64

2.84 6.35 6.06 6.73

Soybean-based cropping systems

CT-Sb NT1-Sb NT2-Sb NT3-Sb

Se/Sb – Se/Sb – Se/Sb – Se/Sb – Se/Sb Mt/Sb+Brz – Brz(2009)/Sb+St – Mt/Sb+St+Sg – Mt/Sb+St – Sr+St (2012)/Sb+St+Sg Mt+/Sb+St – Mt+Cr+St (2009)/Mz+St – Mt/Sb+St – Mt+Cr/Mz+St – Sr+St (2012)/Sb+St Mt/Mz+Brz – Mt/Sb+St – Mt+Cr/Mz+St – St (2011)/Sb+St – Sg+Cr+St (2012)/Mz+St

10.96 36.62 35.47 39.25

2.19 7.32 7.09 7.85

Cassava-based cropping systems

CT-Cs NT1-Cs NT2-Cs NT3-Cs

Cs – Cs – Cs – Cs – Cs Cs+St – Cs+St – Cs+St – Cs+St – Cs+St Cs+St – Mt+St (2009)/Mz+St – St (2010)/Cs+St – Mt+Cr+St (2011)/Mz+St – St (2012)/Cs+St Mt/Mz+St – Cs+St – Mt+Cr+St (2010)/Mz+St – Cs+St – Mt+Cr+St (2012)/Mz3ed c+St

8.06 19.54 21.70 25.27

1.61 3.91 4.34 5.05

Mb: mung bean (Vigna radiata); Rc: rice (Oryza sativa L.); Mt: millet (Pennisetum typhoides Burm); St: Stylosanthes guianensis; Cr: Crotalaria juncea; Mz: maize (Zea mays L.); Se: sesame (Sesamum indicum); Sb: soybean (Glycine max (L.) Merr.); Brz: Brachiaria ruziziensis cv. ruzi; Cs: cassava (Manihot esculenta); Sg: sorghum (Sorghum bicolor L.) ¥ St (Stylosanthes guianensis) left from the year in brackets. “/” indicates relay cropping with varying planting dates; “+” indicates crops planted in association (same or staggered sowing dates).

61

Table 3.2

Mineral fertilizer rates applied to crops during the experiment period (2009–2013)

Annual mineral

fertilizer rate

Crops Year Total fertilizer

inputs 2009 2010 2011 2012 2013

P2O5 (kg ha-1)

N (kg ha-1)

K2O (kg ha-1)

All crops

Rice

Soybean†

Cassava

Maize

Rice

Soybean

Cassava

Maize

80

69

23

92

92

60

60

60

60

32

46

23

69

69

30

60

90

30

32

46

23

69

69

30

60

60

30

32

46

23

69

69

30

60

60

30

32

46

23

69

69

30

60

60

30

208

253

115

368

368

180

300

330

180

† 23 kg N ha-1 were applied at sowing to soybean under NT based systems, not under CT

62

Table 3.3

Soil attributes in 0- to 100-cm depths under reference vegetation and experimental plots in 2011

Land

use

Depth

(cm)

Soil attributes

Sand Silt Clay pH

(CaCl2)

H+Al Al 3+ Ca2+ Mg2+ K+ CEC P

g kg-1 cmol dm-3 mg dm-3

RV

CT†

NT‡

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0.82

1.91

1.30

1.10

0.69

0.55

0.61

1.27

1.38

1.25

0.93

0.78

0.69

0.75

1.52

1.37

1.24

0.83

0.79

0.75

0.65

425

368

334

282

246

224

214

300

293

284

257

240

225

210

306

293

279

252

236

227

219

567

613

653

707

747

770

780

688

693

703

733

752

768

782

680

695

710

740

757

767

775

5.1

5.1

5.0

4.9

4.5

4.4

4.5

4.8

4.8

4.7

4.8

4.8

4.8

4.7

4.8

4.6

4.7

4.7

4.6

4.5

4.4

6.71

6.22

6.06

6.12

6.85

7.52

7.45

7.20

7.29

7.51

6.33

5.96

5.85

5.74

7.19

7.97

7.60

6.54

6.29

6.40

6.81

0.03

0.00

0.03

0.13

0.32

0.60

0.52

0.18

0.18

0.23

0.19

0.21

0.34

0.27

0.17

0.35

0.31

0.24

0.29

0.37

0.49

9.62

7.63

5.35

3.58

2.38

1.93

1.77

4.78

4.69

4.18

3.45

2.78

2.56

2.37

4.67

3.81

3.65

2.84

2.19

1.84

1.54

3.52

2.53

2.12

1.58

1.08

1.08

1.15

1.88

1.66

1.33

1.02

0.81

0.74

0.77

2.23

1.59

1.24

0.91

0.71

0.67

0.80

1.14

0.77

0.56

0.36

0.35

0.35

0.33

0.74

0.62

0.43

0.23

0.12

0.11

0.12

0.81

0.57

0.36

0.19

0.12

0.12

0.13

21.03

17.04

14.05

11.73

10.74

10.92

10.84

14.62

14.41

13.48

11.10

9.67

9.28

8.98

14.97

14.02

12.93

10.54

9.35

9.10

9.32

98.4

68.9

60.7

78.2

79.5

86.0

77.8

55.1

51.5

46.0

45.6

39.2

28.2

29.3

52.08

46.19

46.31

45.43

34.74

30.24

32.65

RV: reference vegetation; CT: conventional plow-based tillage; NT: no-tillage; † and ‡ Mean values of the three CT and nine NT systems, respectively, of three production systems were used for the quantification of soil attributes. CEC (cation exchange capacity) was determined by summation of potential acidity and exchangeable bases (Ca + Mg + K).

63

Table 3.4

Soil bulk density (ρb) (Mg m-3) in 0- to 100-cm soil depths under adjacent reference vegetation

(RV), and rice- (RcCS), soybean- (SbCS) and cassava- (CsCS) based cropping systems in 2011

Land use Soil depth (cm)

0–5 5–10 10–20 20–40 40–60 60–80 80–100

RcCS

RVa

CT-Rcb

NT1-Rc

NT2-Rc

NT3-Rc

SbCS

RVa

CT-Sbb

NT1-Sb

NT2-Sb

NT3-Sb

CsCS

RVa

CT-Csb

NT1-Cs

NT2-Cs

NT3-Cs

1.00 B

1.17 A ns

1.20 A

1.20 A

1.21 A

1.00 B

1.16 A ns

1.16 A

1.16 A

1.14 A

1.00 B

1.10 A ns

1.17 A

1.17 A

1.17 A

1.05 B

1.21 A ns

1.20 A

1.23 A

1.22 A

1.05 B

1.22 A ns

1.25 A

1.19 A

1.18 A

1.05 C

1.11 BCns

1.19 A

1.18 AB

1.18 AB

1.10 B

1.20 A ns

1.20 A

1.18 A

1.22 A

1.10 ns

1.25

1.23

1.16

1.22

1.10 ns

1.15

1.25

1.25

1.24

1.14 ns

1.07

1.10

1.13

1.08

1.14 ns

1.11

1.20

1.11

1.06

1.14 ns

1.12

1.18

1.17

1.13

1.12 A

1.00 C ns

1.05 ABC

1.03 C

1.09 AB

1.12 ns

1.06

1.07

1.08

1.04

1.12 ns

1.02

1.02

1.04

1.03

1.05 ns

1.11

1.05

1.09

1.13

1.05 ns

1.07

1.08

1.09

1.05

1.05 ns

0.99

1.09

1.11

1.04

1.06 ns

1.10

1.08

1.16

1.15

1.06 ns

1.10 ab

1.07 b

1.14 a

1.13 a

1.06 ns

1.06

1.11

1.08

1.09

RV: reference vegetation; CT: conventional plow-based tillage; NT: no-tillage; a Comparison between tillage systems CT, NT1, NT2, NT3 and reference vegetation (RV). Uppercase letters within the same column indicate difference among RV and tillage treatments at P ≤ 0.05 by LSD. b Comparison between tillage systems CT, NT1, NT2 and NT3. Lowercase letters within the same column indicate difference between tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

64

Table 3.5

SOC stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-cm soil depths under rice-based

cropping systems

Soil depth (cm) PE RVa* CT-Rcb NT1-Rc NT2-Rc NT3-Rc

2009

0–5

5–10

10–20

20–40

2011

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

2013

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

8.8

8.8

16.5

22.2

15.5 A

11.2 A

16.0 ns

20.7 ns

13.8 ns

10.3 ns

10.2 A

97.7 A

15.5 A

11.2 ns

16.0 ns

20.7 ns

13.8 ns

10.3 ns

10.2 ns

97.7 ns

9.8 B ns

9.2 B ns

16.0

20.8

14.5

10.2

9.6 A ns

90.1 ABns

10.6 B ns

9.9

18.1

21.3

15.1

11.2

9.8

96.0

9.3 B

8.5 B

15.3

18.3

12.2

8.7

7.3 B

79.6 B

11.2 B

9.0

16.9

19.9

13.2

9.5

8.6

87.4

9.7 B

8.6 B

14.4

17.0

13.6

9.8

9.2 A

82.3 B

11.6 B

10.0

18.3

19.4

15.3

11.0

9.9

95.5

9.5 B

8.4 B

14.4

19.7

13.0

9.4

8.5 AB

82.9 B

12.3 B

9.9

17.1

22.0

14.6

10.9

9.7

96.5

PE: prior to experiment establishment; RV: reference vegetation; CT: conventional tillage; NT: no-till; a Comparison between tillage systems CT-Rc, NT1-Rc, NT2-Rc, NT3-Rc and RV; Uppercase letters within the same line in each cropping system indicate the difference among RV and tillage treatments at P ≤ 0.05 by LSD. * RV collected in 2011 is used for both 2011 and 2013. b Comparison among tillage systems CT-Rc, NT1-Rc, NT2-Rc and NT3-Rc; Lowercase letters within the same line indicate difference between tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

65

Table 3.6

Soil total N stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-cm soil depths under rice-

based cropping systems

Soil depth (cm) PE RVa* CT-Rcb NT1-Rc NT2-Rc NT3-Rc

2009

0–5

5–10

10–20

20–40

2011

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

2013

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

0.90

0.85

1.67

2.60

1.63 A

1.16 A

1.63 ns

2.42 ns

1.72 C

1.41 B

1.36 ns

11.33 ns

1.63 A

1.16 A

1.63 ns

2.42 ns

1.72 ns

1.41 ns

1.36 ns

11.33A

0.91 B ns

0.86 B ns

1.48

2.59

1.96 BC ns

1.62 ABns

1.51

10.93

0.94 B ns

0.83 BC ns

1.50

2.27

1.88

1.77

1.47

10.66 ABns

0.94 B

0.87 B

1.61

2.47

2.06 AB

1.68 AB

1.60

11.23

1.00 B

0.80 C

1.42

2.06

1.68

1.33

1.31

9.60BC

0.94 B

0.90 B

1.62

2.42

2.21 A

1.82 A

1.80

11.71

1.03 B

0.91 B

1.50

2.05

1.51

1.30

1.17

9.47 C

0.98 B

0.94 B

1.61

2.63

2.22 A

1.85 A

1.67

11.90

1.04 B

0.91 B

1.52

2.37

1.99

1.52

1.46

10.81 A


66

Table 3.7

SOC stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-cm soil depths under soybean-


Soil depth (cm) PE RVa* CT-Sbb NT1-Sb NT2-Sb NT3-Sb

2009

0–5

5–10

10–20

20–40

2011

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

2013

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

9.4

9.3

16.8

20.7

15.5 A

11.2 A

16.0 ns

20.7 ns

13.8 ns

10.3 ns

10.2 ns

97.7 ns

15.5 A

11.2 A

16.0 ns

20.7 ns

13.8 ns

10.3 ns

10.2 ns

97.7 ns

9.6 Bc

9.8 B ns

17.4

22.1

14.4

11.0

10.2

94.5

10.1 Cb

9.8 B ns

16.9

23.5

16.4

11.8

10.7

99.2

9.9 Bbc

9.2 B

17.3

23.1

14.7

11.7

11.5

97.4

12.1 Ba

9.8 B

17.2

25.1

16.7

12.8

12.0

105.7

10.4 Ba

9.0 B

15.7

19.1

12.8

9.3

9.1

85.4

11.8 Ba

9.2 B

16.0

22.0

15.1

11.0

9.9

95.0

10.0 Bab

9.0 B

17.0

19.0

12.5

9.1

8.4

85.0

12.4 Ba

9.7 B

16.6

21.7

14.9

10.8

9.5

95.6

PE: prior to experiment establishment; RV: reference vegetation; CT: conventional tillage; NT: no-till; a Comparison between tillage systems CT-Sb, NT1-Sb, NT2-Sb, NT3-Sb and RV; Uppercase letters within the same line in each cropping system indicate the difference among RV and tillage treatments at P ≤ 0.05 by LSD. * RV collected in 2011 is used for both 2011 and 2013. b Comparison among tillage systems CT-Sb, NT1-Sb, NT2-Sb and NT3-Sb; Lowercase letters within the same line indicate difference between tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

67

Table 3.8

Soil total N stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-cm soil depths under

soybean-based cropping systems

Soil depth (cm) PE RVa* CT-Sbb NT1-Sb NT2-Sb NT3-Sb

2009

0–5

5–10

10–20

20–40

2011

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

2013

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

0.89

0.90

1.68

2.33

1.63 A

1.16 A

1.63 ns

2.42 ns

1.72 B

1.41 C

1.36 C

11.33 ns

1.63 A

1.16 A

1.63 ns

2.42 AB

1.72 ns

1.41 BC

1.36 ns

11.33 A

0.93 B ns

0.87 B ns

1.54

2.44

2.12 A ns

1.78 B ns

1.63 B ns

11.31

0.82 Cb

0.72 Cb

1.23

2.04 Bc

1.25

0.92 Cb

0.69

7.67 Bb

0.96 B

0.88 B

1.69

2.79

2.26 A

1.97 A

1.90 A

12.45

0.98 Ba

0.89 Ba

1.57

2.29 Bbc

1.62

1.37 BCab

1.20

9.92 ABab

1.01 B

0.90 B

1.62

2.74

2.20 A

1.85 AB

1.81 AB

12.13

1.02 Ba

0.90 Ba

1.59

2.70 Aab

2.22

2.01 Aa

1.71

12.15 Aa

0.97 B

0.90 B

1.78

2.71

2.10 A

1.80 AB

1.74 AB

12.00

1.08 Ba

0.99 Ba

1.74

2.74 Aa

2.01

1.66 ABa

1.47

11.69 Aa


68

Table 3.9

SOC stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-cm soil depths under cassava-


Soil depth (cm) PE RVa* CT-Csb NT1-Cs NT2-Cs NT3-Cs

2009

0–5

5–10

10–20

20–40

2011

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

2013

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

8.2

8.2

16.8

23.3

15.5 A

11.2 A

16.0 ns

20.7 ns

13.8 ns

10.3 ns

10.2 ns

97.7 ns

15.5 A

11.2 ns

16.0 ns

20.7 ns

13.8 ns

10.3 ns

10.2 ns

97.7 ns

7.8 Cb

8.6 B ns

16.7

22.1

14.9

11.2

10.9

92.2

8.7 Dc

9.7

17.5

22.5

15.7

11.7

11.1

96.9

7.8 Cb

8.0 B

14.3

17.9

13.0

10.3

9.4

80.7

9.7 CDbc

9.2

15.9

20.2

14.9

11.0

10.1

91.0

9.0 BCa

8.8 B

15.9

19.1

13.9

11.5

10.7

88.9

10.1 BCab

9.6

17.8

21.8

15.6

12.3

10.9

98.1

9.3 Ba

8.4 B

14.4

17.7

13.0

10.4

9.7

82.9

11.1 Ba

9.3

16.0

21.3

14.8

11.4

10.1

94.1

PE: prior to experiment establishment; RV: reference vegetation; CT: conventional tillage; NT: no-till; a Comparison between tillage systems CT-Sb, NT1-Cs, NT2-Cs, NT3-Cs and RV; Uppercase letters within the same line in each cropping system indicate the difference among RV and tillage treatments at P ≤ 0.05 by LSD. * RV collected in 2011 is used for both 2011 and 2013. b Comparison among tillage systems CT-Cs, NT1-Cs, NT2-Cs and NT3-Cs; Lowercase letters within the same line indicate difference between tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

69

Table 3.10

Soil total N stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-cm soil depths under

cassava-based cropping systems

Soil depth (cm) PE RVa* CT-Csb NT1-Cs NT2-Cs NT3-Cs

2009

0–5

5–10

10–20

20–40

2011

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

2013

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

0.85

0.80

1.68

2.87

1.63 A

1.16 A

1.63 ns

2.42 ns

1.72 C

1.41 C

1.36 ns

11.33 ns

1.63 A

1.16 A

1.63 ns

2.42 ns

1.72 C

1.41 ns

1.36 ns

11.33 ns

0.78 C ns

0.89 B ns

1.68

2.76

2.26 ABns

1.90 ABns

1.76

12.03

0.78 Cc

0.80 Cb

1.53

2.69

2.04 ABns

1.61

1.61

11.06

0.87 BC

0.90 B

1.70

2.73

2.39 A

2.09 A

1.95

12.63

0.81 Cbc

0.82 Cb

1.45

2.38

1.99 ABC

1.54

1.47

10.46

0.85 BC

0.80 B

1.52

2.33

1.98 BC

1.67 BC

1.71

10.86

0.88 Cb

0.82 Cb

1.47

2.40

1.80 BC

1.52

1.38

10.27

0.95 B

0.87 B

1.59

2.56

2.17 AB

1.89 AB

1.72

11.75

1.02 Ba

0.98 Ba

1.56

2.59

2.13 A

1.73

1.48

11.49


70

Table 3.11

POC stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-cm soil depths under rice-based

cropping systems

Soil depth (cm) RVa* CT-Rcb NT1-Rc NT2-Rc NT3-Rc

2011

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

2013

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

1.70 A

0.67 ns

0.70 ns

0.71 A

0.38 A

0.24 ns

0.24 ns

4.64 A

1.70 A

0.67 ns

0.70 ns

0.71 ns

0.38 ns

0.24 ns

0.24 ns

4.64 ns

0.70 B ns

0.48

0.61

0.39 B ns

0.20 B ns

0.14

0.15

2.67 B ns

1.13 Cb

0.71

0.69

0.44

0.25

0.17

0.18

3.57

0.73 B

0.43

0.61

0.41 B

0.18 B

0.17

0.14

2.67 B

1.31 BCab

0.67

0.83

0.49

0.24

0.22

0.19

3.95

0.83 B

0.50

0.50

0.35 B

0.23 B

0.20

0.15

2.76 B

1.30 BCab

0.74

0.78

0.43

0.28

0.27

0.18

3.98

0.84 B

0.45

0.52

0.45 B

0.19 B

0.12

0.12

2.69 B

1.52 ABa

0.72

0.70

0.53

0.25

0.18

0.18

4.08


71

Table 3.12

MAOC stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-cm soil depths under rice-

based cropping systems in 2011

Soil depth (cm) RVa* CT-Rcb NT1-Rc NT2-Rc NT3-Rc

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

12.01 A

9.47 A

13.69 ns

18.29 ns

11.91 ns

9.34 ns

9.07 A

83.78 A

7.54 B ns

7.37 B ns

12.96

17.11

12.25

9.03

8.06 Aa

74.32 B ns

7.72 B

7.56 B

13.65

16.52

11.28

8.32

6.96 Bb

72.01 B

7.96 B

7.32 B

12.47

14.95

12.20

8.89

8.33 Aa

72.12 B

7.61 B

7.06 B

12.40

16.64

11.37

8.34

6.84 Bb

70.26 B


72

Table 3.13

POC stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-cm soil depths under soybean-


Soil depth (cm) RVa* CT-Sbb NT1-Sb NT2-Sb NT3-S

2011

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

2013

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

1.70 A

0.67 A

0.70 ns

0.71 ns

0.38 A

0.24 ns

0.24 A

4.64 A

1.70 ns

0.67 ns

0.70 ns

0.71 ns

0.38 ns

0.24 ns

0.24 ns

4.64 A

0.69 B ns

0.43 B ns

0.64

0.49

0.17 Bb

0.13

0.14 B ns

2.69 BCns

1.14

0.60

0.75

0.57

0.24

0.18

0.19

3.67 Bns

0.67 B

0.37 B

0.60

0.62

0.35 Aa

0.13

0.14 B

2.86 B

1.34

0.55

0.70

0.67

0.37

0.17

0.20

4.00 B

0.80 B

0.38 B

0.60

0.38

0.15 Bb

0.10

0.12 B

2.53 C

1.36

0.64

0.72

0.49

0.31

0.18

0.16

3.86 B

0.82 B

0.43 B

0.62

0.46

0.20 Bb

0.21

0.14 B

2.88 B

1.42

0.64

0.92

0.56

0.25

0.22

0.18

4.19 AB


73

Table 3.14

MAOC stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-cm soil depths under soybean-


Soil depth (cm) RVa* CT-Sbb NT1-Sb NT2-Sb NT3-S

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

12.01 A

9.47 A

13.69 ns

18.29 ns

11.91 B

9.34 ns

9.07 ns

83.78 ns

7.75 Cc

7.96 B ns

14.71

19.09

12.04 Bb

10.62

9.08

81.25

8.21 BCb

7.96 B

15.09

20.27

13.66 Aa

10.65

10.36

86.20

8.70 Ba

7.76 B

13.76

16.91

11.59 Bb

8.63

8.22

75.57

8.36 BCab

7.63 B

14.17

16.52

11.15 Bb

8.00

7.57

73.40


74

Table 3.15

POC stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-cm soil depths under cassava-


Soil depth (cm) RVa* CT-Csb NT1-Cs NT2-Cs NT3-Cs

2011

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

2013

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

1.70 A

0.67 ns

0.70 ns

0.71 A

0.38 A

0.24 ns

0.24 ns

4.64 A

1.70 A

0.67 ns

0.70 ns

0.71 ns

0.38 A

0.24 ns

0.24 ns

4.64 A

0.42 B ns

0.47

0.70

0.47 B ns

0.22 B ns

0.14

0.19

2.61 B ns

0.55 Dc

0.62

0.78

0.52

0.27 B

0.17

0.21

3.12 Bns

0.43 B

0.43

0.60

0.33 B

0.18 B

0.12

0.16

2.25 B

0.82 CDbc

0.59

0.68

0.39

0.22 B

0.15

0.18

3.03 B

0.51 B

0.58

0.69

0.51 AB

0.17 B

0.24

0.28

2.98 B

0.86 Cb

0.69

0.75

0.54

0.21 B

0.27

0.31

3.63 B

0.98 B

0.43

0.57

0.29 B

0.20 B

0.13

0.33

2.93 B

1.25 Aa

0.68

0.67

0.50

0.24 B

0.18

0.31

3.83 AB


75

Table 3.16

MAOC stocks (Mg ha-1), on an equivalent soil-depth, in 0- to 100-cm soil depths under cassava-


Soil depth (cm) RVa* CT-Csb NT1-Cs NT2-Cs NT3-Cs

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

12.01 A

9.47 A

13.69 ns

18.29 ns

11.91 ns

9.34 ns

9.07 ns

83.78 ns

6.87 B ns

7.27 B ns

14.49

19.56

13.21

9.93

9.83

81.16

6.76 B

6.95 B

12.42

15.68

11.55

9.20

8.53

71.09

7.57 B

7.56 B

14.21

17.12

12.41

10.28

9.53

78.68

7.20 B

7.08 B

12.48

14.78

11.04

8.78

8.36

69.72


76

CHAPTER 4

Sensitivity of Labile Soil Organic Carbon Pools and Enzymatic Activities

to Short-term Conservation Agriculture Cropping Systems

Abstract

Soil organic carbon (SOC) pools, particularly labile pools, and soil enzymes are good

indicators of short-term impacts of soil management practices. The objective of this study was to

investigate the sensitivity of the labile SOC pool (i.e., hot-water extractable C - HWEOC and

permanganate oxidizable C - POXC) and soil enzyme activities (i.e., β-glucosidase,

arylsulfatase) to changes in soil management and crop rotations with diverse crop residue inputs

in rice-, soybean- and cassava-based cropping systems (RcCS, SbCS, and CsCS, respectively).

The four treatments in each cropping system consisted of (a) conventional tillage (CT); (b) no-till

(NT): one year frequency pattern of main crops; and (c) and (d) NT: bi-annual rotations of main

crops with maize. The field trials were initiated in 2009 and the measurements of labile SOC

pools were conducted in 2011 and 2013, and pyrophosphate extractable organic C (PEOC),

chemically stabilized organic C (CSOC) and soil enzyme activities only in 2011. On average, the

results showed greater HWEOC stocks by 61%, 55% and 53%, and POXC stocks by 23%, 21%

and 32% in NT than in CT soils under RcCS, SbCS and CsCS, respectively, at 0-5 cm soil layer

after five years. PEOC and CSOC stocks were almost constant in each depth among treatments,

except 0-5 cm in CsCS. β-glucosidase activity was 18%, 28% and 49% greater in NT than in CT

soils at 0-5 cm under RcCS, SbCS, CsCS, respectively, whereas arylsulfatase activity was 36%

and 39% in NT than in CT under SbCS and CsCS, respectively, but no significant differences in

RcCS. Compared among three NT treatments, bi-annual crop rotations showed a better

increasing trend of HWEOC, POXC and enzymatic activities than one-year frequency pattern. In

77

conclusion, short-term NT crop rotations with permanent soil cover significantly increased the

storage of HWEOC and POXC and enhanced β-glucosidase and arylsulfatase activities at the

surface soil layer with a potential at the subsoil layers as a result of higher biomass-C inputs and

the absence of soil disruption. Thus, the labile SOC pool and soil enzymes could be served as

sensitive indicators of SOC dynamics to short-term changes in soil management and crop

rotations.

4.1 Introduction

Soils can be either a sink for or a source of CO2 depending on land use and management

(Lal, 2003b, 2010). Changes in agricultural management practices might determine either

function of soils due to their important contribution to the soil C sequestration process. In

Cambodia, the development of annual upland crops (i.e., maize, cassava, soybean, and

mungbean) to satisfy the needs of expanding population soared from ~ 217K ha in 2003 to ~

716K ha in 2012 (MAFF, 2013). This leads to forest clearance to expand the agricultural land

that has exacerbated the growing concern over land degradation (Belfield et al., 2013; Hean,

2004; Poffenberger, 2009; UNDP, 2010). The particular challenges to evaluate land productivity,

to improve soil health and to sequester soil C are necessary to define sustainable agricultural

practices in this country. Globally, there is a growing interest in development of agricultural

management practices to sequester atmospheric CO2 into soil C (Lal, 2008a) and the extent to

which soils can sequester C varies with soil mineralogy, net primary production (Tivet, Sá, Lal,

Borszowskei, et al., 2013), climate, cropping systems and tillage practices (Wright, Hons,

Lemon, McFarland, & Nichols, 2008). Soil organic C (SOC) plays a crucial role in enhancing

crop productivity (Lal, 2003b) as a result of its profound impacts on soil physical, chemical and

biological properties (Ayuke et al., 2011; Lal, 2008b; Lienhard et al., 2013; Six et al., 2004;

78

Tisdall & Oades, 1982). Frequent conventional tillage (CT) hastens SOC mineralization due to

greater exposure to microbial oxidation (Green et al., 2007; Jastrow, Boutton, & Miller, 1996;

Reicosky et al., 1995) resulting from the breakdown of soil aggregates (Zotarelli et al., 2007) and

marked changes in soil environment (i.e., temperature, moisture and oxygen), thus increasing

soil microbial biomass and activity (D. Guo et al., 2013) and causing a drastic increase in C

efflux from soil to the atmosphere (Lal & Logan, 1995). This SOC decline causes poor

aggregation, acceleration in soil erosion, and reduced soil biological and enzymatic activities

(Ghani et al., 2003).

SOC can be enhanced by crop rotations and no-tillage (NT) practices due to addition of

biomass-C inputs into the soil via crop residues near the soil surface and the absence of soil

disruption (Lal et al., 2003). Without massive supplies of organic materials, it is extremely

difficult to sequester SOC in arable soils (Powlson et al., 2011). Conservation agriculture (CA)

holds tremendous potential for sustainable soil management through the application of its three

key principles: (a) continuous minimal mechanical soil disturbance (no-tillage), (b) permanent

organic soil cover, and (c) diversified crop rotations grown in sequence or associations (FAO,

2008). The CA practices increase annual C inputs through plant roots, root exudates and

aboveground plant residues, and decrease SOC decomposition rates through increased soil

aggregation and a protection of SOC from decomposers. The impacts of CA or its different

component practices have been reviewed as a set of improved agricultural practices to potentially

sequester C into the soils in various regions (Corsi et al., 2012; Govaerts et al., 2009; Lal, 2006;

Luo et al., 2010; Ogle et al., 2012).

To assess SOC dynamics under CA, several indicators of SOC pools and enzymatic

activities have recently received more attention due to their sensitivity to soil management

79

practices. The SOC pool is highly diverse with contrasting turnover times, and stabilized or

protected against microbial decomposition (Lützow et al., 2006). A better understanding of the

short-term impacts of CA on SOC dynamics necessitates separation of SOC into pools. Active or

SOC labile pool might be potentially restored even in a short period because it is the most rapid

turnover times and its oxidation drives the flux of CO2 between soils and atmosphere. Its

sensitivity better explains soil biological effects on soil properties and SOC dynamics compared

with total SOC (Campbell et al., 1997; Z. Huang et al., 2008), thus serving as an indicator of

future changes in total SOC (Campbell et al., 1997). Hot-water extractable organic C (HWEOC)

is a sensitive indicator of SOC quality and constitutes the readily-decomposable SOM (Ghani et

al., 2003). It responds rapidly to changes in C supply (Jinbo et al., 2006) and indicates the effect

of land use on soil organic matter (SOM) quality (Gregorich, Monreal, Carter, Angers, & Ellert,

1994). The dissolved organic C, microbial biomass, soluble soil carbohydrates and amines are

extracted from soil during the extraction of HWEOC (Ghani et al., 2003). Similarly,

permanganate oxidizable carbon (POXC) is also an active SOC pool and correlates with soil

microbial activity including soil microbial biomass C (SMBC), soluble carbohydrate C and total

C (Weil et al., 2003). Positive relationships between SMBC and HWEOC (Ghani et al., 2003;

Ghani et al., 2010; Sparling et al., 1998), between SMBC and POXC (Culman et al., 2010;

Melero et al., 2009), and between SOC and labile pools (i.e., HWEOC and POXC) (Culman et

al., 2012; Sá et al., 2014; Tirol-Padre & Ladha, 2004; Weil et al., 2003) have been reported. For

example, the studies by Sá et al. (2014) in a subtropical region and by Tivet, Sá, Lal,

Borszowskei, et al. (2013) in tropical and subtropical regions indicated a high potential of NT

systems with cover crops to restore the labile SOC pool (i.e., HWEOC, POXC) in the soil

surface layers. The increased labile SOC pool under NT cropping systems can be the pathway to

80

sequester C from the atmosphere to soils and to decrease the release of SOC back to the

atmosphere.

Soil enzymes play a substantial role in organic matter mineralization through a wide

range of metabolic processes (María et al., 2002) and their activities are sensors of SOM

decomposition in the soil system by providing information about microbial status and soil

physicochemical conditions (Sinsabaugh et al., 2008). Sources of soil enzymes include living

and dead microorganisms, plant roots and plant residues, and soil animals (Das & Varma, 2011).

NT, high residue return and crop rotations have been reported to enhance enzymatic activities.

Soil enzymes respond to soil management changes more quickly than other soil quality indicator

changes and detection (Dick, 1994; Ndiaye et al., 2000). Arylsulfatase (EC 3.1.6.1) plays a role

in S cycling and can catalyze the hydrolysis of organic sulfate esters (M. A. Tabatabai &

Bremner, 1970). High organic C inputs via crop residues constitute a principal reservoir of

sulfate esters, the substrate for arylsulfatase that involves in the mineralization of ester sulfate.

(Dick et al., 1997). β-glucosidase (EC 3.2.1.21) plays a role in the C cycle and is closely related

to the transformation and accumulation of SOM (Wang & Lu, 2006) because it is regarded as the

most abundant extracellular enzyme in soil (Busto & Perez‐Mateos, 2000). Green et al. (2007)

found that β-glucosidase activity was greater in the soil under NT than that under disk plow in

the tropical Savannah.

At tropical temperatures, SOM is broken down ten times faster, allowing for more rapid

biomass growth but resulting in a smaller soil C pool compared with temperate climate (Malhi,

Baldocchi, & Jarvis, 1999). Short-term changes in total SOC due to soil management practices

are often difficult to detect (Zotarelli et al., 2007). However, the short-term effects of CA on

labile SOC pool (i.e., HWEOC, POXC) and soil enzymatic activities remain debatable. The

81

combination of labile SOC pool and enzymatic activities can provide valuable information to

assess short-term SOC dynamics and the estimation over long-term trends. Thus, this study

aimed to investigate the sensitivity of labile SOC pool and soil enzyme activities changes to soil

management and crop rotations with diverse crop residue inputs in rice-, soybean- and cassava-

based cropping systems.


Detailed descriptions of the site, experiments, biomass-C inputs and soil sampling are

reported in Chapter 3. Briefly, the field experiments were initiated in 2009 in a Latosol at

Bosknor Research Station in Kampong Cham Province, Cambodia (Latitude 12°12′30″N,

longitude 105°19′7″E and 118 m elevation). The adjacent reference vegetation (RV) was located

~ 500 m from the experimental plots (latitude 12°12′13″N, longitude 105°19′11″E and 118 m

elevation). The vegetation composition was the old coffee plantation under the shade of

Leucaena glauca which was grown since 1990 and was selected as a baseline to assess the

management-induced changes in SOC pools and enzymatic activities.

The experiments distinctly comprised of (a) rice- (b) soybean- and (c) cassava-based

cropping systems (RcCS, SbCS, CsCS, respectively). The three-replicated experimental plots

were laid out in randomized complete block design with four treatments in each cropping system

consisting of (a) conventional tillage (CT) in which the main crops were planted in annual

succession for rice and soybean (i.e., mungbean/rice–CT-Rc, sesame/soybean–CT-Sb) and

mono-cropping for cassava (CT-Cs); (b) no-till (NT) in which main crops were planted in a one

year frequency pattern (NT1-Rc, NT1-Sb, NT1-Cs); and (c) and (d) NT in which main crops

were planted in bi-annual rotations with maize, the two plots in these bi-annual rotations being

NT2-Rc, NT3-Rc for rice, NT2-Sb, NT3-Sb for soybean and NT2-Cs, NT3-Cs for cassava. Basal

82

P fertilizer application was done by surface banding with thermo phosphate (i.e., 16% P2O5, 31%

CaO and 16% MgO), and fractioned top dressing on main crops for N and K, using urea (46 %

N) and potassium chloride (60 % K2O), respectively. The total fertilizer input (2009-2013) was

208 kg ha-1 P2O5, 253 kg ha-1 N, 180 kg ha-1 K2O5 for rice, 208 kg ha-1 P2O5, 115 kg ha-1 N, 300

kg ha-1 K2O5 for soybean, 208 kg ha-1 P2O5, 368 kg ha-1 N, 330 kg ha-1 K2O5 for cassava, and 208

kg ha-1 P2O5, 368 kg ha-1 N, 180 kg ha-1 K2O5 for maize. The aboveground biomass of main and

cover crops were measured and the belowground biomass was estimated on the basis of the root

to shoot ratio (RS ratio) index. The details of accumulative and annual biomass-C inputs (2009–

2013) in each cropping system are presented in Table 4.1.

Soil samples at seven depths: 0-5, 5-10, 10-20, 20-40, 40-60, 60-80, and 80-100 cm were

collected during November 2011 and 2013. Bulk samples were oven-dried at 40 ºC, gently

ground, sieved through a 2-mm sieve and homogenized. Due to high clay content of the studied

soil, it was assumed that the bulk density did not significantly change within this two-year period

(2011-2013). Thus, soil bulk density (ρb) was measured only in 2011 and used to calculate PEOC

and CSOC stocks in 2011, and HWEOC and POXC stocks in both 2011 and 2013 by computing

on an equivalent soil mass-depth basis described by Ellert and Bettany (1995).

4.2.1 Soil organic C pool extraction and analysis. Different SOC pools were isolated

by (a) hot-water extractable organic C (HWEOC), (b) permanganate oxidizable C (POXC), (c)

(sodium) pyrosphospate extractable organic C (PEOC) and (d) the chemically stabilized organic

C (CSOC) extracted by H2O2 oxidation. The analyses of HWEOC, POXC and PEOC were

conducted in a sequence using the soil sample in the same tube.

4.2.1.1 Hot-water extractable organic C. The HWEOC was determined by the method

adapted from Ghani et al. (2003). Briefly, 1.5 g of 2 mm-sieved bulk soil was weighed into a 15

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mL polypropylene centrifuge tube. The sample was treated with 9 mL of distilled water for 16

hours at 80 ºC. Each tube was then shaken on a vortex shaker for 10 sec to ensure that the

HWEOC released from the SOC was fully suspended in the solution. The tubes were centrifuged

for 10 min at 4000 rpm. The SOC in the centrifuged extracts was oxidized by potassium

dichromate in sulfuric acid and back titrated with ferrous sulfate.

4.2.1.2 Permanganate oxidizable C. The determination of POXC is adapted from Tirol-

Padre and Ladha (2004) and Culman et al. (2012). After the extraction of HWEOC, the

remaining supernatant in each tube was discarded and 10 mL of a stock solution of KMnO4 (60

mM) was added to the sediments in the same tubes and shaken on a vortex shaker for 15 sec to

suspend the soil in the stock solution. The tubes were horizontally shaken on a table shaker at

200 rpm for 15 min at room temperature, and then centrifuged for 10 min at 4000 rpm. 2 mL of

the supernatant was pipetted and transferred to a 125 mL Erlenmeyer flask and diluted with 100

mL deionized water. The absorbance of the solutions was determined at 565 nm using Visible

Spectrophotometer (SP-1105), and the amount of the oxidized organic C was calculated from the

KMnO4 consumed. The conversion of the absorbance to POXC concentration (g kg-1) was done

by using a standard calibration curve, based on the linear relationship between KMnO4

concentrations vs. absorbance at 565 nm. The amount of POXC was computed as follow:

POXC (g kg-1) = [(mM blank – mM sample) × (125/2) × 10 × 9] / [1000 (mL L-1) × wt of sample

(g)]

Where, mM blank and mM sample are the concentrations (mmol L-1) of KMnO4 in the

blank and sample, respectively, determined from the standard regression curve; 125/2 = the

dilution factor (mL mL-1); 10 = the volume (mL) of KMnO4 added to the soil sample; 9 = the

amount of C oxidized from every mole of KMnO4 (g mol-1 or mg mmol-1).

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4.2.1.3 (Sodium) Pyrophosphate extractable organic C. The determination of PEOC is

adapted from Bascomb (1968) and McKeague, Brydon, and Miles (1971), using only the

samples collected in 2011. After removal of the KMnO4 supernatant, the KMnO4 residue in the

sediments was washed out with deionized water for 3-4 times. Then, a third extraction was

performed by adding 10 mL of 0.1 M sodium pyrophosphate (Na4P2O7) solution into the same

tubes and shaken on a vortex shaker for 15 sec to suspend the soil in the solution. The tubes were

horizontally shaken on a table shaker at 120 rpm for 6 hours at room temperature, and then

centrifuged for 10 min at 4000 rpm. The SOC in the centrifuged extracts was oxidized by

potassium dichromate in sulfuric acid and back titrated with ferrous sulfate. The SOC dissolved

in the pyrophosphate extract corresponds to the SOC associated with the active forms of Al and

Fe.

4.2.1.4 Chemically stabilized organic C. The determination of CSOC was based on the

method by Jagadamma, Lal, Ussiri, Trumbore, and Mestelan (2010) using only the samples

collected in 2011. Briefly, 1 g of bulk soil was wetted with 10 mL of distilled water for 10 min.

Then, 30 mL of H2O2 at 10% was added, and the solution was kept at 50 ºC using a water bath.

Each sample was manually shaken daily to ensure a good oxidation, and additional H2O2 was

added if necessary. The oxidation period, using H2O2 as the oxidizing agent, requires several

days and depends on texture, mineralogy, the pre-existing SOC concentration, and the nature and

quantity of the C inputs. The oxidation was stopped when the frothing completely subsided. The

sample was then washed thrice with 30 mL distilled water and oven-dried at 40 ºC until constant

weight. The sample weight was recorded. The sample was finely ground for C determination by

the dry combustion method using an elemental CN analyzer (TruSpec CN, LECO, St. Joseph,

USA).

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4.2.2 Assay of soil enzyme activities. The soil enzyme activities were measured at three

soil depths, 0–5, 5–10 and 10–20 cm using the same composite soil samples used to analyze

SOC pools.

4.2.2.1 β-glucosidase. Activity of β-glucosidase (EC 3.2.1.21, β-d-glucoside

glucohydrolase) was assayed according to the method of Eivazi and Tabatabai (1988). Briefly, 1

g of dry soil (< 2 mm) was placed into a 50 mL flask, and then 4 mL of pH 6.0 of modified

universal buffer (MUB) and 1 mL of 0.05 M p-nitrophenyl-β-D-glucoside (PNG) solution were

added. The flask was swirled to fully mix the contents, stoppered, and incubated at 37 ºC for 1

hour. Then, 1 mL of 0.5 M CaCl2 and 4 mL of 0.1 M pH 12 tris (hydroxymethyl) aminomethane

(THAM) buffer were added to stop the reaction. The soil suspension was allowed to develop a

yellow color and filtered. The color intensity was determined using a spectrophotometer at 400

nm. β-glucosidase activity was reported on a dry soil basis with units of mg p-nitrophenol kg-1

soil h-1.

4.2.2.2 Arylsulfatase. Arylsulfatase (EC 3.1.6.1., arylsufate sulfohydrolase) activity was

assayed according to the method of M. A. Tabatabai and Bremner (1970). Briefly, 1 g of dry soil

(< 2 mm) was placed into a 50 mL flask, and incubated with 4 mL of 0.5 M acetate buffer (pH

5.8) and 1 mL of 0.05 M p-nitrophenol (PN) sulfate solution at 37 ºC for 1 hour. Then, 1 mL of

0.5 M CaCl2 and 4 mL of 0.5 M pH 12 NaOH were added to stop the reaction. The PN released

was extracted and filtered, and the color intensity was determined using a spectrophotometer at

400 nm. Arylsulfatase activity was quantified as mass (mg) of p-nitrophenol being produced by

enzymatic hydrolysis of potassium p-nitrophenyl sulfate during 1 hour incubation per unit mass

(kg dry soil; PNP equivalents).

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4.2.3 Statistical analysis. The statistical analysis was performed using SAS 9.2 statistical

software. To compare the effects of tillage and crop sequence treatments of cropping system at

each depth, data were independently subjected to analysis of variance procedures with

randomized complete block design, and comparisons among treatment means were calculated

based on least significant difference test (LSD) at the 0.05 probability level, unless otherwise

stated.

4.3 Results

4.3.1 Soil organic C pools (HWEOC, POXC, PEOC, CSOC).

4.3.1.1 Rice-based cropping systems. Tillage and crop rotation treatments had a

significant (P<0.05) effect on HWEOC concentrations at the 0-5 cm soil layer in 2013 (Figure

4.1b). The increasing trend of higher accumulation was observed in 2011, in which soils under

NT averagely had 12% more HWEOC concentrations at 0-5 cm and the bi-annual crop rotations

(NT2-Rc and NT3-Rc) tended to accumulate more than NT1-Rc. In the subsoil layers, the

differences were not evident among treatments. In 2013, soil under NT1-Rc, NT2-Rc, and NT3-

Rc contained 46%, 60%, and 76%, respectively, greater HWEOC concentrations than CT-Rc at

0-5 cm soil depth. The increasing trend was also observed at 5-10 cm depth. On an average, NT-

Rc soils had 42% more HWEOC than CT-Rc soil. Significant effects of tillage and crop rotation

treatments on HWEOC stocks were detected at 0-5 cm depth in both 2011 and 2013 (P<0.05)

(Table 4.2). In 2011, HWEOC stocks under NT3-Rc were significantly greater than that under

CT-Rc, but not those under NT1-Rc and NT2-Rc. On average in 2013, NT-Rc soils contained

61% higher than CT-Rc. There was a consistent increase in HWEOC stocks in the three NT

treatments until 20 cm soil depth but a decrease in CT-Rc. The RV soil had significantly higher

HWEOC stocks than cultivated soils (i.e., NT-Rc, CT-Rc) at 0-5 and 5-10 cm depths in 2011 but

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only 0-5 cm depth in 2013. Soil under RV contained 69%, 63%, 44%, and 40% higher HWEOC

stocks than CT-Rc, NT1-Rc, NT2-Rc, and NT3-Rc, respectively, at 0-5 cm in 2011. At 5-10 cm,

RV also had 48% and 39% greater HWEOC than CT-Rc and NT-Rc (i.e., NT1-Rc, NT2-Rc,

NT3-Rc). In 2013, soil under RV had 96% and 21% higher HWEOC stocks than those under

CT-Rc and NT-Rc, respectively, at 0-5 cm depth. Considering the 100 cm as a single stratum,

HWEOC stocks under RV were significantly greater than under cultivated soils in 2011 but not

in 2013 while no significant differences were detected among NT-Rc and CT-Rc. On average,

NT-Rc had 10% and 20% more HWEOC stocks in 2011 and 2013, respectively, compared with

CT-Rc.

NT-Rc significantly increased POXC concentrations and stocks at 0-5, 40-60 and 60-80

and 80-100 cm depths in 2011 and only 0-5 cm depth in 2013 (Figure 4.1). In 2011, POXC

concentration in soils under NT-Rc was 14% greater than that under CT-Rc. The noticeable

increasing trend also appeared at 5-10 cm depth, at which soils under NT-Rc had 11% higher

POXC. In 2013, soils under NT1-Rc, NT2-Rc, and NT3-Rc had 18%, 21%, and 24%,

respectively, significantly higher POXC concentrations than under CT-Rc at 0-5 cm depth.

POXC tended to increase in all treatments at 0-20 cm depths interval from 2011 to 2013. The

CT-Rc soil contained lower POXC stocks of 0.15 Mg ha-1 in 2011 and 0.23 Mg ha-1 in 2013 than

NT-Rc soils at 0-5 cm depth (Table 4.3). The same trend was observed 5-10 and 10-20 soil

depths. NT-Rc resulted in a higher trend of increasing POXC stocks in the subsoil layers

compared with CT-Rc in 2013. POXC stocks under RV were 0.62 and 0.47 Mg ha-1 at 0-5 cm

and 0.31 and 0.20 Mg ha-1 at 5-10 cm greater than under CT-Rc and NT-Rc, respectively, in

2011. As a result of increased biomass-C inputs, the differences decreased by 5% and 20% at 0-5

cm, and by 8% and 13% at 5-10 cm depth under CT-Rc and NT-Rc in 2013, respectively.

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Considering the 100 cm as a single stratum, NT-Rc soils reserved 7% and 14% more POXC

stocks than that of CT-Rc in 2011 and 2013, respectively. Comparing to RV, POXC stocks under

NT-Rc, particularly bi-annual rotations, showed a surpassing trend over that under RV in 2013.

Differences in tillage and crop rotations did not significantly affect the changes in

concentrations and stocks of PEOC and CSOC in all depths (Figure 4.1a and Table 4.4).

Although, they did not differ, the increasing trend of PEOC concentrations was observed in bi-

annual crop rotations compared with CT-Rc. On average, they accumulated 6% at 0-5 cm and

7% at 5-10 cm depth higher PEOC than CT-Rc soil. Unlike PEOC, CT-Rc soil contained more

CSOC concentrations in most depths compared with NT-Rc soils. At 0-5 and 5-10 cm depths,

soils under CT-Rc had 13% and 9%, respectively, more CSOC concentrations than under NT-Rc.

PEOC stocks in all treatments were almost constant in all depths but significantly lower than

those under RV. Soils under RV significantly stored 70%, 41%, 45%, and 29% greater PEOC

stocks than cultivated soils at 0-5, 5-10, 10-20, and 20-40 cm depths, respectively. In contrast to

PEOC, significant differences in CSOC between RV and cultivated soils were not detected. Soils

under CT-Rc tended to store more CSOC in the two surface layers. Considering the 100 cm as a

single stratum, RV soil had 4.0 and 1.17 Mg ha-1 more PEOC and CSOC stocks than cultivated

soils, respectively. Overall, the mean portions of the SOC pools for RV and treatments and

depths ranged in the order CSOC > POXC > PEOC > HWEOC.

4.3.1.2 Soybean-based cropping systems. Significant (P < 0.05) effects of tillage and

crop rotations on HWEOC concentrations were detected at the 0-5 cm depth in 2013 (Figure

4.2b). On average, bi-annual rotations (NT2-Sb and NT3-Sb) contained 16% more HWEOC than

CT-Sb soil while only 3% under NT1-Sb at 0-5 cm depth in 2011. In 2013, HWEOC

concentrations were higher by 52%, 50%, and 64% under NT1-Sb, NT2-Sb, and NT3-Sb,

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respectively, compared with that under CT-Sb at 0-5 cm depth. An increasing trend was also

observed at 5-10 cm depth. There was no clear evidence of significant differences in the subsoil

layers in both 2011 and 2013. The significant differences in HWEOC stocks were detected at 5-

10 cm depth in 2011 (P < 0.05) and 0-5 cm depth in 2013 (P < 0.05) (Table 4.5). Although they

did not differ at 0-5 cm depth in 2011, NT-Sb (NT1-Sb, NT2-Sb, NT3-Sb) tended to store 12%

higher HWEOC than that of CT-Sb. This increasing trend was apparent in 2013. On average,

HWEOC stocks under NT-Sb were 55% (0.15 Mg ha-1) greater than that under CT-Sb. From

2011 to 2013, HWEOC stocks decreased by 7% under CT-Sb but increased by 29% under NT-

Sb. When comparing to RV, HWEOC stocks under RV were greater than CT-Sb and NT-Sb by

63% and 46% at 0-5 cm, and by 48% and 52% at 5-10 cm depth, respectively, in 2011. This

trend was changed in 2013, in which the differences were increased by 12% in CT-Sb but

decreased 33% in NT-Sb. Considering the 100 cm as a single stratum, there were no significant

differences in HWEOC stocks between RV and cultivated soils (i.e., CT-Sb, NT-Sb). However,

bio-annual crop rotation treatments tended to increase more HEWOC than CT-Sb.

Differences in tillage and crop rotations resulted in significant changes in POXC

concentrations at 0-5 cm depth in 2011, and at 0-5 and 10-20 cm depths in 2013 (Figure 4.2).

Soil under NT3-Sb accumulated 19% greater POXC than that under CT-Sb. Although they did

not differ, soils under NT1-Sb and NT2-Sb quantitatively had 11% and 14% more POXC than

that under CT-Sb. The increasing trend was evident in 2013, in which POXC concentrations

under NT-Sb soils were 21% higher than CT-Sb. There were no noticeable variations in the

deeper soil layers in both 2011 and 2013. In general, POXC stocks showed no significant

differences among treatments at most depths except 60-80 cm in 2011, and 0-5 and 10-20 cm in

2013 (Table 4.6). The trend of increasing POXC stocks under NT-Sb was still observed at 0-5

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cm depth in 2011. On average, NT-Sb soils stored 14% more POXC than CT-Sb. In 2013, NT-

Sb had 21% greater POXC stocks than CT-Sb. Similar trend was also found at 10-20 cm depth.

When comparing to RV, soils under RV had greater POXC stocks than under CT-Sb and NT-Sb

by 54% and 35% in 2011, and 54% and 27% in 2013, respectively, at 0-5 cm depth. Considering

the 100 cm as a single stratum, POXC stocks were almost constant among RV and cultivated

soils in 2011. However, significant differences were detected in 2013 but RV did not differ from

NT-Sb.

Similar to RcCS, no significant differences in PEOC and CSOC concentrations and

stocks were detected after three years (Figure 4.2a and Table 4.7). The average PEOC

concentration under NT-Sb soils was 2.75 g kg-1 which was 4% more than that under CT-Sb. The

concentrations decreased with increasing depths but there was no clear evidence of the

differences or even an increasing trend in the subsoil layers. Similarly, both CT-Sb and NT-Sb

soils showed constant CSOC concentrations in each depth and it ranged from 5.15 to 5.29 g kg-1

at 0-5 cm depth and 3.42 to 3.85 g kg-1 at 80-100 cm depth. RV soils significantly had 39%

greater PEOC stocks than cultivated soils only at 0-5 cm depth. In contrast, RV and cultivated

soils had no significant differences in CSOC stocks at all depths. Considering the 100 cm as a

single stratum, PEOC and CSOC stocks did not differ between RV and cultivated soils.

4.3.1.3 Cassava-based cropping systems. Significant effects of tillage and crop rotations

on HWEOC concentrations and stocks were detected at only 0-5 cm depth in both 2011 and 2013

(P < 0.01 and P < 0.05, respectively; Figure 4.3 and Table 4.8). On average, the bi-annual crop

rotation treatments (NT2-Cs and NT3-Cs) accumulated 17% higher HWEOC concentrations than

CT-Cs in 2011 and it increased to 58% in 2013 at the 0-5 cm depth. The significant increase also

observed in NT1-Cs which had 42% greater HWEOC than CT-Cs at 0-5 cm depth in 2013. Soils

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under CT-Cs showed a decrease in HWEOC concentration by 12% at 0-5 cm and 9% at 5-10 cm

depth from 2011 to 2013. However, there were noticeable changes in the deeper soil layers

among the treatments. HWEOC stocks under NT-Cs (i.e., NT1-Cs, NT2-Cs, NT3-Cs) averagely

increased 20% from 2011 to 2013. Soils under RV had greater HWEOC than CT-Cs and NT-Cs

by 69% and 48% in 2011, and 88% and 24% in 2013, respectively. Considering the 100 cm as a

single stratum, RV and cultivated soils (i.e., CT-Cs, NT-Cs) did not significantly differ in both

sampling times. The increase in HWEOC was observed in NT-Cs. The stocks ranged in the order

RV > NT-Cs > CT-Cs.

The differences in tillage and crop rotations resulted in significant effects on POXC

concentrations and stocks at 0-5, 40-60, 60-80 and 80-100 cm depths in 2011, and only 0-5 cm

depth in 2013 (Figure 4.3 and Table 4.9). On average, the bi-annual rotations accumulated 18%

and 15% higher POXC than CT-Cs and NT1-Cs soils, respectively, in 2011 and the differences

increased to 40% for CT-Cs and 25% for NT1-Cs in 2013. Similar to the surface 0-5 cm, NT2-

Cs and NT3-Cs showed a greater accumulation of POXC in the 40-100 depths interval in 2011

and their increasing trend was observed in the subsoil layers in 2013. From 2011 to 2013, POXC

stocks increased 5%, 11% and 18% under NT1-Cs, NT2-Cs, and NT3-Cs, respectively, at 0-5 cm

depth. In contrast, 4% decrease was observed in soil under CT-Cs. However, there were no

noticeable changes in POXC stocks in the subsoil layers. At 0-5 cm depth, RV soils stored

significantly higher POXC by 70%, 65%, 42%, and 46% under CT-Cs, NT1-Cs, NT2-Cs, and

NT3-Cs, respectively, in 2011. The adoption of NT systems increased POXC stocks in 2013 by

decreasing the differences by 8%, 14%, and 22% under NT1-Cs, NT2-Cs, and NT3-Cs,

respectively; at 0-5 cm depth compared with those under RV in 2011, but 7% depletion of POXC

was observed in CT-Cs. Considering the 100 cm as a single stratum, significant differences in

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POXC stocks were detected in 2011. The soils under RV and bi-annual crop rotations treatments

stored 11% and 8%, respectively, significantly greater than CT-Cs. In 2013, RV and NT-Cs soils

still showed an increasing trend compared to CT-Cs.

PEOC concentrations and stocks were influenced by tillage and crop rotations at 0-5 cm

depth but the significant differences in CSOC concentrations and stocks were not detected in all

soil depths (Figure 4.3a and Table 4.10). PEOC concentrations under bi-annual rotations were

12% and 7% greater than those under CT-Cs and NT1-Cs, respectively, at 0-5 cm depth. An

increasing trend under NT-Cs was also observed at 5-40 cm depths interval. RV soils had greater

PEOC stocks than CT-Cs, NT1-Cs, NT2-Cs, and NT3-Cs by 62%, 55%, 44%, and 44% at 0-5

cm, and by 25%, 22%, 14%, and 11% at 5-10 cm depth, respectively. The different trend of

PEOC under RV and treated soils was not apparent in the deeper soil layers. Similar to RcCS and

SbCS, CSOC concentrations and stocks were nearly constant among treatments in all depths.

Considering the 100 cm as a single stratum, PEOC and CSOC stocks were almost constant

between RV and cultivated soils. It indicated that short-term NT with different crop rotations did

not alter the changes in PEOC and CSOC in 100 cm soil depth.

4.3.2 Soil enzyme activities (β-glucosidase and arylsulfatase).

4.3.2.1 Rice-based cropping systems. β-glucosidase activity was significantly influenced

by tillage and crop rotations at 0-5 cm depth, with the average of NT-Rc being 18% greater than

CT-Rc while there were no significant differences in the subsoil layers. In contrast, arylsulfatase

activity was not found to be significantly different at all depths after three years (Table 4.11).

However, the increasing trend of arylsulfatase activity under NT-Rc soils was observed at 0-5

cm depth, at which NT-Rc tended to have 5% greater than CT-Rc. The surpassing trend of the

two enzyme activities under NT-Rc over CT-Rc at 5-10 and 10-20 cm depths was not apparent.

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When comparing to RV, β-glucosidase activity under RV soils was greater than CT-Rc, NT1-Rc,

NT2-Rc, and NT3-Rc by 158%, 124%, 119%, and 115%, respectively, at 0-5 cm depth while it

was greater by 80% and 72% under CT-Rc and NT-Rc soils at 5-10 cm depth. Similarly,

arylsulfatase activity under RV soil was 63% and 57% greater than cultivated soils at 0-5 and 5-

10 cm depths, respectively. Even with greater β-glucosidase and arylsulfatase activities in two

surface layers under RV, no significant differences were detected at 10-20 cm depth.

4.3.2.2 Soybean-based cropping systems. β-glucosidase and arylsulfatase activities were

significantly increased by NT-Sb compared with CT-Sb at 0-5 cm depth (Table 4.12). β-

glucosidase activity under bi-annual crop rotation treatments (NT2-Sb and NT3-Sb) was 31%

greater than CT-Sb. Its activity under CT-Sb and NT1-Sb did not significantly differ. However,

NT1-Sb showed an increasing trend of 22% higher activity than CT-Sb. Similarly, average

arylsulfatase activity under NT2-Sb and NT3-Sb was 46% greater than under CT-Sb while the

increasing trend was apparent in NT1-Sb. The two enzymes activities were almost constant in

the two subsoil layers. When comparing to RV, β-glucosidase activity under RV was

significantly greater than under CT-Sb and NT-Sb by 174% and 114% at 0-5 cm, by 75% and

74% at 5-10 cm, and by 18% and 19% at 10-20 cm depth, respectively. Similarly, arylsulfatase

activity was also found to be significantly different from CT-Sb and NT-Sb soils by 102% and

48% at 0-5 cm, and by 55% and 46% at 5-10 cm depth, respectively.

4.3.2.3 Cassava-based cropping systems. Significant effects of tillage and crop rotations

on β-glucosidase and arylsulfatase activities were detected only at 0-5 cm depth, with β-

glucosidase activity under NT2-Cs and NT3-Cs being 54% and 60%, and with arylsulfatase

activity being 47% and 49%, respectively, greater than those under CT-Cs (Table 4.13). The

increasing trend of β-glucosidase activity under NT-Cs soils was observed at 5-10 cm depth but

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not arylsulfatase activity. The activities of two enzymes were almost constant at the 10-20 cm

depth. When comparing to RV, β-glucosidase activity under RV was greater than those under

CT-Cs and NT-Cs by 241% and 130% at 0-5 cm, and by 106% and 67% at 5-10 cm,

respectively. Similarly, arylsulfatase activity under RV was also greater than those under CT-Cs

and NT-Cs by 138% and 71% at 0-5 cm, and by 61% and 46% at 5-10 cm depth, respectively.

Even with greater enzymatic activities in the two surface layers, no significant differences were

observed between RV and treated soils at 10-20 cm depth.

4.4 Discussion

4.4.1 Changes in hot-water extractable organic C, permanganate oxidizable C,

pyrophosphate extractable organic C, and chemically stabilized organic C. HWEOC is

representative of SMBC, containing more microbial-derived than acid hydrolysable

carbohydrates (Haynes & Francis, 1993). Later, this finding was confirmed by some studies

including those by Ghani et al. (2003) and Sparling et al. (1998) who emphasized positive

correlation between SMBC and HWEOC. In addition, SMBC is also correlated well with POXC

(Melero et al., 2009; Weil et al., 2003). Labile SOC pool is sensitive to changes in soil

management practices so it can be served as an indicator to short-term impacts of agricultural

management practices (i.e., NT cropping systems with cover crops). In the present study,

HWEOC and POXC were able to differentiate the impact of short-term NT cropping systems.

We observed a significant increase in HWEOC and POXC stocks after five years of the three

intensive NT crop rotations with diversified cover crops in the surface soil layer in the three

cropping systems. The possible contributing factor could be the continuous supply of biomass-C

in the NT systems that might influence an increase in these two labile SOC pools due to higher

aboveground and root inputs with enhanced crop intensity than CT that could stimulate of

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microbial activity (Lienhard et al., 2013). The increase in HWEOC could contribute to the

changes in SOC due to its positive correlation with SOC (Sparling et al., 1998). CT practices

decreased HWEOC stocks in the topsoil by 14%, 7% and 1% in RcCS, SbCS and CsCS,

respectively, in two years (2011-2013; see Table 4.8). Explanations for this result may include

less biomass-C inputs via crop residues under CT compared with NT, thus decreasing the supply

of carbohydrates for microorganisms and soil enzyme activity resulting in a reduction in SMBC

which correlates with HWEOC. Our finding contradicts the study by Salinas-Garcia et al. (2000)

who indicated that the greater concentration of SMBC under NT practices than under CT

resulted from higher accumulation of crop residues at the soil surface after six years in a dry

tropical region of Mexico. Rhizodeposition of root mass and exudates greatly influences C

turnover in soils (Kuzyakov, Ehrensberger, & Stahr, 2001) that could affect the net accumulation

of HWEOC in soil rhizosphere (Ghani et al., 2003). In general, an increasing trend of HWEOC

accumulation in the a few subsurface layers under NT was observed compared with CT. This

was probably due to the incorporation of deep-rooted cover crops such as Congo grass, millet,

sorghum, and sunhemp into crop rotations under NT practices in the three cropping systems.

Continuous input of root biomass and exudates from these cover crops could contribute to the

increase in HWEOC. Séguy et al. (2006) reported that SOC in the subsoil could be sequestered

by higher SOC rhizodeposition of the deep rooting systems such as Congo grass and sorghum

and Crotalaria spp. Similarly, intensive NT cropping systems also significantly increased

POXC. Soils under NT averagely had 20%, 21%, and 32% greater POXC stocks than those

obtained in CT at the 0-5 cm depth under RcCS, SbCS, and CsCS, respectively, after five years.

This was probably the fact that accumulation of POXC results from the rate of biomass-C inputs

from crop biomass, a major source of SOC, returned to the soil and the absence of soil

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disturbance under NT that reduced SOC mineralization. Even three years longer but in the

similar soil type and climatic condition, these results are consistent with the study by Tivet, Sá,

Lal, Borszowskei, et al. (2013) who reported a significantly increased HWEOC at 0-5 cm soil

depth after eight-year intensive NT systems (e.g., diversity of cover/relay crops and high annual

biomass input). Similar effects was also observed in the study by Stine and Weil (2002) in a

tropical region of south central Honduras who found POXC was highly correlated to SOC and

soils under NT contained grater POXC than CT emphasizing that changes in SOC resulted from

proportional changes in both active and passive C fractions. Soil aggregate stability positively

correlates to residue restitution and fungal and bacterial densities under NT systems (Lienhard et

al., 2013), HWEOC (Haynes & Swift, 1990) and POXC (Stine & Weil, 2002). Thus, the greater

HWEOC and POXC under NT systems maybe consequently enhance soil aggregate formation

which may protect SOC (Tivet, Sá, Lal, Briedis, et al., 2013). The consistent effect of NT crop

rotations with cover crops on HWEOC and POXC, after five years of management suggests that

this this labile SOC pool (i.e., HWEOC, POXC) may be useful in assessing SOC dynamics of

short-term changes in soil management practices, particularly the soil surface layer.

Pyrophosphate has been used to extract soil C due to its selective ability to remove Fe

and Al-bound organic matter by complexing with di- and trivalent cations (Wattel-Koekkoek et

al., 2001). Thus, PEOC pool represents the SOC associated with the active forms of Fe and Al.

In the present study, PEOC were almost constant in each depth among treatments in RcCS and

SbCS. However, it showed an increase under bi-annual crop rotations treatments at 0-5 cm depth

in CsCS. The PEOC stocks averagely comprised of 16% of SOC stocks in 2011 (data not shown)

and were comparable to POXC stocks in all cropping systems, demonstrating a potential of this

clayed Cambodian Oxisol to function as a sink for SOC that could be related to the formation of

97

complexes with the active forms of Fe and Al. Erich, Plante, Fernández, Mallory, and Ohno

(2012) reported that PEOC likely represented the material that was chemically sorbed to soil

surfaces and protected from decomposition due to this sorption. CSOC also known as the passive

or refractory SOM pool is organic substances which is resistant to further mineralization

(Eusterhues et al., 2005). Our results indicated that CSOC was almost constant among treatments

at each soil layer. The CSOC concentrations ranged from 3.22 to 5.98 g kg-1, 3.42 to 5.29 g kg-1

and 3.37 to 5.24 g kg-1 in RcCS, SbCS, and CsCS, respectively. These results of CSOC

concentrations were in the ranges reported by Tivet, Sá, Lal, Borszowskei, et al. (2013) in a

subtropical Oxisol and a tropical Latosol and by Eusterhues et al. (2005) in the temperate

Cambisol and Podzol. This finding could be explained that the amount of young plant residue-

derived SOC added to the soil from crop residues within three years did not affect CSOC,

suggesting that CSOC in the C baseline could be related to chemical and morphological structure

of SOM and chemical and physical nature of the soil minerals. The high clay contents of soils

used in the present study were almost constant in each depth in the three cropping systems (Hok

et al., under review). Clay minerals have a high specific surface area and carry a charge enabling

them to bind, and thereby chemically stabilize SOM (Wattel-Koekkoek et al., 2001). The study

of peroxide oxidation of clay-associated organic matter by Plante, Chenu, Balabane, Mariotti,

and Righi (2004) found that there was no relationship between the proportion of hydrogen

peroxide-resistant SOM and C depletion in a cultivation chronosequence. In general, our results

showed a slight decrease with increasing depths in the three cropping systems. The slightly

higher CSOC in the surface layers in this study was probably due to the fresh aliphatic plant

materials resistant to H2O2 oxidation (Eusterhues et al., 2005) because the oxidation process was

done with the bulk soil without prior removal of the labile SOC pool.

98

4.4.2 Changes in β-glucosidase and arylsulfatase. The enzyme activities in soil systems

vary primarily due to different amounts of organic matter content and composition, living

organisms’ activity and intensity of biological processes (Das & Varma, 2011). They are

sensitive indicators providing information on the impact of land use management and cropping

systems (Fernandes et al., 2005; Rabary et al., 2008). In the present study, it is consistent that

tillage and crop rotations only affected β-glucosidase activity in the surface soil layer when NT

had 18%, 28%, and 49% higher than CT in RcCS, SbCS, and CsCS, respectively. This could be

explained by the fact that biomass-C supplies from crop residues contained the readily available

substrate such as carbohydrate that could increase this enzyme activity. Roldán et al. (2003)

found that β-glucosidase is stimulated where crop residues are left intact on the soil surface. This

result confirms the previous investigation that direct seeding mulch-based cropping systems with

living much and crop residue significantly increased β-glucosidase activity (on average 121%

greater) compared with CT systems at 0-5 cm soil depth over a 12-year period in a cold tropical

climate of Madagascar (Rabary et al., 2008). A similar finding was also observed by Green et al.

(2007) who found that the β-glucosidase activity in the soil under a NT corn-common bean

rotation was 82% significantly greater than under disk plow management in the 0-5 cm depth

after five-year NT practices in a red Latosol in the tropical Savannah. Our results also showed a

decrease in β-glucosidase activity with increasing depths where NT systems mostly maintained

an increasing trend over CT, except under NT3-Cs which was already significantly greater than

that obtained in the CT-Cs. Although the residues were not mechanically incorporated with the

soil, the restitution of crop residues on the soil surface led to a slow incorporation of organic

materials into the soil. Together with root biomass and exudates, the significant increase in β-

glucosidase activity in the subsoil layers might be apparent with longer time. Thus, NT cropping

99

systems with permanent soil cover provide a good potential to enhance β-glucosidase activity not

only in the top soil but also in the subsoil layers as the results of increasing trend was already

observed in this study.

High biomass-C inputs constitute a principal reservoir of sulfate esters, the substrate for

arylsulfatase (Dick et al., 1997), the enzyme being involved in mineralization of ester sulfate in

the soil (M .A. Tabatabai, 1994). In the present study, NT practices maintained greater

arylsulfatase activity in the surface layer in SbCS and CsCS. Although they did not differ in

RcCS, NT still showed an increasing trend of 5% compared to CT. This greater arylsulfatase

activity might result from the increase of SMBC from the higher crop residues under NT systems

due to its relations to an increase in HWEOC and POXC. High organic matter inputs via crop

residues tend to increase SMBC due to continuous provision of energy sources for

microorganisms (Vaughan & Ord, 1985). The microbial biomass consists mostly of bacteria and

fungi. Fungi and bacteria have about 42% and 10%, respectively, of its S as ester sulfate, the

substrate for arylsulfatase (Saggar, Bettany, & Stewart, 1981). Some previous studies found a

strong positive correlation between SMBC and arylsulfatase activity (Ekenler & Tabatabai,

2003; Gajda et al., 2013; Li & Sarah, 2003). In contrast to the result of this study, Green et al.

(2007) who reported that there was no significant changes in arylsulfatase activity under five-

year NT systems compared to disk plow systems in tropical Savannah. This was probably due to

low biomass-C inputs since their study was conducted in a corn-common bean rotation without

incorporation of other forage crops as soil cover unlike our study. However, the study in

temperate soils by Gajda et al. (2013) indicated that arylsulfatase activity under eight-year NT

systems was two- to threefold greater than that obtained under traditional tillage at 0-15 cm soil

layer as a result of higher organic C inputs via plant residues. The significant effect of NT crop

100

rotations with diversified cover crops on β-glucosidase and arylsulfatase activities in this study

suggests that the two enzymes are good indicators to assess the effect of short-term NT crop

rotations on the biological activity of soil, particularly the soil surface layer.

4.5 Conclusions

Short-term intensive NT cropping systems with permanent soil cover are likely to play a

substantial role in increasing the storage of HWEOC and POXC and improving β-glucosidase

and arylsulfatase activities, especially at the 0-5 cm soil layer. When comparing among NT

systems, bi-annual crop rotations might be recommended as an appropriate crop rotation scheme

in the studied soil type. The size of SOC pools and enzymatic activities decrease with increasing

depths. These results emphasize the positive impact of the absence of soil disturbance under NT

and the importance of the cover crops and their residues cropped in association or rotations with

main crops to significantly accumulate more labile SOC pools and change the biological

functioning of the soil, with higher soil enzyme activities in the surface layers. The increase in

the two SOC pools might lead to increase soil aggregate stability that physically protects SOC

and consequently to sequester total SOC. Incorporation of deep-rooting cover crops into crop

rotation might be evident with time to potentially enhance the labile SOC pools and enzymatic

activities in the subsoil layers. Thus, these two SOC pools and soil enzymes could serve as

sensitive indices of management effects on SOC dynamics of short-term changes in agricultural

management practices. Unlike labile SOC pool and soil enzymes, PEOC and CSOC were almost

constant in each depth among treatments indicating that these two SOC pools might not be

affected by short-term changes in soil management practices in the studied soil type. However,

their applicability to other soil types, climatic conditions and agricultural management practices

must be evaluated.

101

Figure 4.1. Concentrations of (a) hot water-extractable organic C (HWEOC) and permanganate

oxidizable C (POXC), pyrophosphate extractable organic C and chemically stabilized organic C

(CSOC) in 2011, and (b) HWEOC and POXC in 2013 in 0- to 100-cm depth under rice- based

cropping systems. Error bars represent the standard error of the mean.

0 3 6 9 12 15

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

0-5

5-1

01

0-2

02

0-40

40-6

060

-80

80-1

00

SOC concentrations (g kg-1)

Dep

th (

cm)

CSOCPEOCPOXCHWEOC

0 1 2 3 4 5

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

RVCT-Rc

NT1-RcNT2-RcNT3-Rc

0-5

5-1

01

0-2

02

0-40

40-6

060

-80

80-1

00


Dep

th (

cm)

POXC

HWEOC

(a) (b)

102



(CSOC) in 2011, and (b) HWEOC and POXC in (b) 2013 in 0- to 100-cm depth under soybean-

based cropping systems. Error bars represent the standard error of the mean.

0 3 6 9 12 15

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

0-5

5-1

010

-20

20-4

040

-60

60-

80

80-

100


Dep

th (

cm)

CSOCPEOCPOXCHWEOC

0 1 2 3 4 5

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

RVCT-Sb

NT1-SbNT2-SbNT3-Sb

0-5

5-1

010

-20

20-4

040

-60

60-

80

80-

100


Dep

th (

cm)

POXC

HWEOC

(a) (b)

103



(CSOC) in 2011, and (b) HWEOC and POXC in 2013 in 0- to 100-cm depth under cassava-

based cropping systems. Error bars represent the standard error of the mean.

0 3 6 9 12 15

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

0-5

5-1

010

-20

20-4

040

-60

60-

80

80-

100


Dep

th (

cm)

CSOCPEOCPOXCHWEOC

0 1 2 3 4 5

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

RVCT-Cs

NT1-CsNT2-CsNT3-Cs

0-5

5-1

010

-20

20-4

040

-60

60-

80

80-

100


Dep

th (

cm)

POXC

HWEOC

(a) (b)

104

Table 4.1

Land use, crop sequence, and carbon input in the five-year experiment period (2009-2013)

C input (Mg ha-1)




Mb/Rc – Mb/Rc – Mb/Rc – Mb/Rc – Mb/Rc Mt/Rc+St – Mt+Cr/Rc+St – St(2010)/Rc+St – St(2011)¥/Rc+St – Mt+St(2012)/Rc+St Mt/Rc+St – Mt+Cr+St (2009)/Mz+St – Mt+Cr+St (2010)/Rc+St – St(2011)/Mz+St – St (2012)/Rc+St Mt/Mz+St – Mt+Cr+St (2009)/Rc+St – St (2010)/Mz+St – St (2011)/Rc+St – St (2012)/Mz+St

14.22 31.75 30.29 33.64

2.84 6.35 6.06 6.73



Se/Sb – Se/Sb – Se/Sb – Se/Sb – Se/Sb Mt/Sb+Brz – Brz(2009)/Sb+St – Mt/Sb+St+Sg – Mt/Sb+St – Sr+St (2012)/Sb+St+Sg Mt+/Sb+St – Mt+Cr+St (2009)/Mz+St – Mt/Sb+St – Mt+Cr/Mz+St – Sr+St (2012)/Sb+St Mt/Mz+Brz – Mt/Sb+St – Mt+Cr/Mz+St – St (2011)/Sb+St – Sg+Cr+St (2012)/Mz+St

10.96 36.62 35.47 39.25

2.19 7.32 7.09 7.85



Cs – Cs – Cs – Cs – Cs Cs+St – Cs+St – Cs+St – Cs+St – Cs+St Cs+St – Mt+St (2009)/Mz+St – St (2010)/Cs+St – Mt+Cr+St (2011)/Mz+St – St (2012)/Cs+St Mt/Mz+St – Cs+St – Mt+Cr+St (2010)/Mz+St – Cs+St – Mt+Cr+St (2012)/Mz3ed c+St

8.06 19.54 21.70 25.27

1.61 3.91 4.34 5.05


105

Table 4.2

Hot water-extractable organic C (HWEOC) stocks in 0- to 100-cm depth under rice-based

cropping systems at two sampling time (2011 and 2013)

HWEOC (Mg C ha-1)

Year Depth (cm) RVa CT-Rcb NT1-Rc NT2-Rc NT3-Rc

2011

2013

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

0.49 A

0.43 A

0.50 ns

0.85 ns

0.73 ns

0.53 ns

0.55 AB

4.08 A

0.49 A

0.43 ns

0.50 ns

0.85 ns

0.73 ns

0.53 ns

0.55 ns

4.08 ns

0.29 Bb

0.29 Bns

0.54

0.81

0.57

0.45

0.54 Bns

3.49 Bns

0.25 Cb

0.25

0.44

0.76

0.68

0.53

0.47

3.38

0.30 Bab

0.30 B

0.49

0.85

0.74

0.57

0.55 AB

3.80 AB

0.37 Ba

0.33

0.54

0.81

0.76

0.63

0.62

4.06

0.34 Bab

0.32 B

0.46

0.74

0.77

0.60

0.66 A

3.89 AB

0.40 ABa

0.34

0.54

0.80

0.77

0.56

0.55

3.96

0.35 Ba

0.31 B

0.50

0.79

0.69

0.59

0.63 AB

3.86 AB

0.44 ABa

0.37

0.61

0.85

0.77

0.57

0.58

4.19

RV: reference vegetation; CT: conventional tillage; NT: no-till; a Comparison between tillage systems CT-Rc, NT1-Rc, NT2-Rc, NT3-Rc and reference vegetation (RV). Uppercase letters within the same row indicate difference among RV and tillage treatments at P ≤ 0.05 by LSD. b Comparison between tillage systems CT-Rc, NT1-Rc, NT2-Rc and NT3-Rc. Lowercase letters within the same row indicate difference between tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

106

Table 4.3

Permanganate oxidizable C (POXC) stocks in 0- to 100-cm depth under rice-based cropping

systems at two sampling time (2011 and 2013)

POXC (Mg C ha-1)

Year Depth (cm) RVa CT-Rcb NT1-Rc NT2-Rc NT3-Rc

2011

2013

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

1.65 A

1.30 A

2.12 ns

3.52 ns

3.15 AB

2.97 AB

2.99 ns

17.70 ns

1.65 A

1.30 A

2.12 ns

3.52 ns

3.15 ns

2.97 ns

2.99 ns

17.70 ns

1.03 Cb

0.99 Cns

1.98

3.40

2.95 Bb

2.65 Cb

2.60 b

15.60

1.14 Cb

1.06 Bns

2.05

3.32

2.99

2.74

2.72

16.02

1.18 Ba

1.11 BC

2.09

3.41

3.12 ABb

2.78 BCab

2.70 ab

16.39

1.34 Ba

1.22 A

2.35

3.58

3.27

2.99

2.89

17.64

1.16 Ba

1.07 BC

2.01

3.47

3.37 Aa

3.00 Aa

3.12 a

17.20

1.37 Ba

1.22 A

2.38

3.84

3.42

3.12

3.11

18.46

1.19 Ba

1.13 B

2.09

3.41

3.04 Bb

2.78 BCb

2.69 ab

16.33

1.41 Ba

1.27 A

2.33

3.97

3.49

3.02

3.02

18.51


107

Table 4.4

Stocks of pyrophosphate extractable organic C (PEOC) and chemically stabilized organic C

(CSOC) in 0- to 100-cm depth under rice-based cropping systems in 2011

SOC pools

Depth (cm) RVa CT-Rcb NT1-Rc NT2-Rc NT3-Rc

PEOC (Mg C ha-1)

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

1.89 A

1.48 A

2.68 A

4.04 A

2.83 ns

1.63 ns

1.41 ns

15.96 A

1.10 Bns

1.03 Bns

1.91 Bns

3.16 Bns

2.21

1.64

1.26

12.31 Bns

1.06 B

0.97 B

1.80 B

3.25 B

2.17

1.62

1.30

12.17 B

1.17 B

1.13 B

1.92 B

3.10 B

2.14

1.33

1.21

12.00 B

1.14 B

1.07 B

1.74 B

3.03 B

2.06

1.22

1.07

11.33 B

CSOC (Mg C ha-1)

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

2.55 ns

2.48 ns

5.09 ns

9.64 ns

8.43 ns

7.75ns

7.56 ns

43.50 ns

2.98

2.63

4.73

9.88

8.76

7.35

6.83

43.16

2.63

2.38

4.61

9.21

8.16

7.37

7.13

41.49

2.66

2.36

5.02

8.96

8.60

7.55

7.28

42.43

2.64

2.51

4.64

9.44

8.28

7.65

7.08

42.24


108

Table 4.5

Hot water-extractable organic C (HWEOC) stocks in 0- to 100-cm depth under soybean-based


HWEOC (Mg C ha-1)

Year Depth (cm) RVa CT-Sbb NT1-Sb NT2-Sb NT3-Sb

2011

2013

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

0.49 A

0.43 A

0.50 ns

0.85 ns

0.73 ns

0.53 ns

0.55 ns

4.08 ns

0.49 A

0.43 ns

0.50 ns

0.85 ns

0.73 ns

0.53 ns

0.55 ns

4.08 ns

0.30 Bns

0.29 BCab

0.49

0.87

0.58

0.49

0.53

3.55

0.28 Bb

0.27

0.41

0.83

0.78

0.70

0.64

3.91

0.31 B

0.27 BCb

0.47

0.81

0.71

0.51

0.42

3.50

0.42 Aa

0.34

0.49

0.89

0.65

0.63

0.54

3.96

0.34 B

0.26 Cb

0.49

0.83

0.71

0.65

0.54

3.82

0.42 Aa

0.38

0.49

0.78

0.71

0.68

0.62

4.08

0.36 B

0.32 Ba

0.47

0.87

0.74

0.71

0.54

4.01

0.46 Aa

0.39

0.53

0.82

0.74

0.63

0.70

4.27

RV: reference vegetation; CT: conventional tillage; NT: no-till; a Comparison between tillage systems CT-Sb, NT1-Sb, NT2-Sb, NT3-Sb and reference vegetation (RV). Uppercase letters within the same row indicate difference among RV and tillage treatments at P ≤ 0.05 by LSD. b Comparison between tillage systems CT-Sb, NT1-Sb, NT2-Sb and NT3-Sb. Lowercase letters within the same row indicate difference between tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

109

Table 4.6

Permanganate oxidizable C (POXC) stocks in 0- to 100-cm depth under soybean-based cropping


POXC (Mg C ha-1)

Year Depth (cm) RVa CT-Sbb NT1-Sb NT2-Sb NT3-Sb

2011

2013

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

1.65 A

1.30 A

2.12 ns

3.52 ns

3.15 ns

2.97 B

2.99 ns

17.70 ns

1.65 A

1.30 ns

2.12 ns

3.52 ns

3.15 ns

2.97 ns

2.99 ns

17.70 AB

1.07 Cns

1.09 Bns

2.09

3.59

3.29

2.97 Bb

2.93

17.03

1.07 Cb

1.12

1.94 b

3.62

3.31

3.00

2.91

16.95 Bb

1.18 BC

1.09 B

2.12

3.55

3.15

2.95 Bb

2.91

16.95

1.29 Ba

1.17

2.12 ab

3.70

3.48

2.96

3.10

17.82 ABab

1.22 BC

1.08 B

2.13

3.59

3.37

3.24 Aa

3.23

17.86

1.26 Ba

1.15

2.17 a

3.75

3.41

3.19

3.23

18.14 ABab

1.27 B

1.10 B

2.15

3.87

3.52

3.06 ABab

3.04

18.01

1.34 Ba

1.22

2.27 a

4.04

3.54

3.33

3.25

18.99 Aa


110

Table 4.7


(CSOC) in 0- to 100-cm depth under soybean-based cropping systems in 2011

SOC pools

Depth (cm) RVa CT-Sbb NT1-Sb NT2-Sb NT3-Sb

PEOC (Mg C ha-1)

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

1.89 A

1.48 ns

2.68 ns

4.04 ns

2.83 ns

1.63 ns

1.41 ns

15.96 ns

1.33 Bns

1.35

2.50

3.77

2.54

1.34

1.21

14.04

1.35 B

1.35

2.61

3.76

2.49

1.42

1.07

14.05

1.39 B

1.37

2.63

3.95

2.65

1.45

1.25

14.69

1.38 B

1.37

2.57

3.98

2.80

1.44

1.29

14.83

CSOC (Mg C ha-1)

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

2.55 ns

2.48 ns

5.09 ns

9.64 ns

8.43 ns

7.75 ns

7.56 ns

43.50 ns

2.61

2.44

4.78

9.34

8.66

7.42

7.30

42.55

2.64

2.58

4.59

9.97

8.81

7.41

7.29

43.29

2.60

2.39

4.58

9.27

9.21

8.02

8.24

44.31

2.57

2.50

4.85

9.66

9.11

8.09

7.75

44.53


111

Table 4.8

Hot water-extractable organic C (HWEOC) stocks in 0- to 100-cm depth under cassava-based


HWEOC (Mg C ha-1)

Year Depth (cm) RVa CT-Csb NT1-Cs NT2-Cs NT3-Cs

2011

2013

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

0.49 A

0.43 A

0.50 ns

0.85 ns

0.73 ns

0.53 ns

0.55 ns

4.08 ns

0.49 A

0.43 ns

0.50 ns

0.85 ns

0.73 ns

0.53 ns

0.55 ns

4.08 ns

0.29 Cb

0.29 Bns

0.47

0.80

0.54

0.49

0.51

3.39

0.26 Cb

0.26

0.48

0.74

0.66

0.53

0.48

3.41

0.30 Cb

0.29 B

0.48

0.90

0.70

0.66

0.51

3.84

0.37 Ba

0.29

0.55

0.82

0.71

0.64

0.61

3.99

0.34 Ba

0.29 B

0.47

0.76

0.76

0.63

0.58

3.83

0.40 ABa

0.34

0.56

0.89

0.73

0.61

0.54

4.07

0.35 Ba

0.30 B

0.44

0.81

0.62

0.62

0.63

3.77

0.42 ABa

0.36

0.53

0.82

0.75

0.63

0.59

4.10

RV: reference vegetation; CT: conventional tillage; NT: no-till; a Comparison between tillage systems CT-Cs, NT1-Cs, NT2-Cs, NT3-Cs and reference vegetation (RV). Uppercase letters within the same row indicate difference among RV and tillage treatments at P ≤ 0.05 by LSD. b Comparison between tillage systems CT-Cs, NT1-Cs, NT2-Cs and NT3-Cs. Lowercase letters within the same row indicate difference between tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

112

Table 4.9

Permanganate oxidizable C (POXC) stocks in 0- to 100-cm depth under cassava-based cropping


POXC (Mg C ha-1)

Year Depth (cm) RVa CTb NT1 NT2 NT3

2011

2013

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

1.65 A

1.30 ns

2.12 ns

3.52 ns

3.15 B

2.97 BC

2.99 BC

17.70 AB

1.65 A

1.30 ns

2.12 ns

3.52 ns

3.15 ns

2.97 ns

2.99 ns

17.70 ns

0.97 Cb

0.99

2.03

3.52

2.95 Cc

2.77 Cc

2.78 Cc

16.01 Cns

0.93 Cb

1.02

1.89

3.49

3.10

2.88

2.80

16.11

1.00 Cb

1.01

1.89

3.36

3.18 Bb

2.97 BCbc

2.98 BCbc

16.39 BC

1.05 Cb

1.03

1.95

3.44

3.09

3.02

2.91

16.49

1.16 Ba

1.14

2.18

3.73

3.49 Aa

3.22 Aa

3.28 Aa

18.20 A

1.29 Ba

1.19

2.20

3.74

3.16

3.10

2.98

17.66

1.13 Ba

1.12

2.10

3.62

3.34 Aa

3.09 ABab

3.09 ABab

17.49 AB

1.33 Ba

1.16

2.22

3.80

3.32

3.09

2.94

17.86


113

Table 4.10


(CSOC) in 0- to 100-cm depth under cassava-based cropping systems in 2011

SOC pools

Depth (cm) RVa CTb NT1 NT2 NT3

PEOC (Mg C ha-1)

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

1.89 A

1.48 A

2.68 ns

4.04 ns

2.83 ns

1.63 ns

1.41 ns

15.96 ns

1.17 Bb

1.18 C ns

2.24

3.83

2.75

1.82

1.67

14.66

1.22 Bb

1.21 BC

2.29

4.04

2.81

1.61

1.53

14.71

1.31 Ba

1.30 BC

2.39

4.03

2.59

1.60

1.39

14.61

1.31 Ba

1.33 AB

2.48

4.13

2.67

1.80

1.47

15.18

CSOC (Mg C ha-1)

0–5

5–10

10–20

20–40

40–60

60–80

80–100

0-100

2.55 ns

2.48 ns

5.09 ns

9.64 ns

8.43 ns

7.75 ns

7.56 ns

43.50 ns

2.60

2.45

5.04

9.33

8.84

7.69

7.53

43.48

2.52

2.50

4.87

9.36

8.89

7.81

7.71

43.66

2.62

2.49

4.64

9.39

9.06

7.66

7.15

43.01

2.52

2.53

5.05

9.54

8.79

7.58

8.05

44.06


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Table 4.11

β-glucosidase and arylsulfatase activities at 0- to 20-cm depth under rice-based cropping

systems in 2011

Enzymatic activities

Depth (cm) RVa CT-Rcb NT1-Rc NT2-Rc NT3-Rc

β-glucosidase (mg PNP kg-1 soil h-1)

0–5

5–10

10–20

80.1 A

50.3 A

25.1 ns

31.0 Cb

27.9 Bns

19.0

35.7 BCa

29.4 B

19.8

36.6 Ba

29.1 B

18.8

37.2 Ba

29.2 B

20.0

Arylsulfatase (mg PNP kg-1 soil h-1)

0–5

5–10

10–20

26.6 A

19.7 A

9.9 ns

15.8 Bns

12.3 Bns

7.7

16.1 B

12.0 B

7.5

16.7 B

12.4 B

7.7

16.8 B

13.4 B

8.1


115

Table 4.12

β-glucosidase and arylsulfatase activities at 0- to 20-cm depth under soybean-based cropping

systems in 2011


Depth (cm) RVa CT-Sbb NT1-Sb NT2-Sb NT3-Sb


0–5

5–10

10–20

80.1 A

50.3 A

25.1 A

29.2 Cb

28.7 Bns

21.2 ABns

35.6 BCab

28.9 B

21.7 AB

38.3 Ba

28.1 B

20.6 B

38.2 Ba

29.7 B

21.2 AB


0–5

5–10

10–20

26.6 A

19.7 A

9.9 ns

13.2 Cb

12.7 B

8.1

15.3 BCab

13.3 B

8.1

19.4 Ba

13.6 B

8.3

19.1 Ba

13.7 B

8.3


116

Table 4.13

β-glucosidase and arylsulfatase activities at 0- to 20-cm depth under cassava-based cropping

systems in 2011


Depth (cm) RVa CT-Csb NT1-Cs NT2-Cs NT3-Cs


0–5

5–10

10–20

80.1 A

50.3 A

25.1 ns

23.5 Cb

24.4 Cns

20.8

30.9 BCab

26.4 BC

19.5

36.2 Ba

31.1 BC

21.8

37.6 Ba

32.9 B

22.4


0–5

5–10

10–20

26.6 A

19.7 A

9.9 ns

11.2 Db

12.2 Bns

7.3

13.5 CDb

13.0 B

7.3

16.5 BCa

13.5 B

7.8

16.7 Ba

14.0 B

8.7


117

CHAPTER 5

Dynamics of Soil Aggregate-associated Organic Carbon under Short-term

Conservation Agriculture Cropping Systems

Abstract

Conservation agriculture has a potential to enhance soil aggregation and consequently to

sequester soil organic C (SOC). Changes in the proportions of water stable soil aggregates and

aggregate-associated SOC, total N and permanganate oxidizable C (POXC) due to soil

management (i.e., conventional tillage – CT, no-till – NT) and crop rotations in rice-, soybean-

and cassava-based cropping systems (RcCS, SbCS and CsCS, respectively) were studied in a

clayed soil. There were four treatments in each cropping system comprising of one CT and three

NT treatments arraying in randomized complete block design with three replicates. Soil

aggregate samples were collected in 0-5, 5-10 and 10-20 cm depths after three years of the

experiments. On average, the proportions of large macroaggregates (8-19 mm) in the 0-5 cm

depth under NT increased 23%, 39% and 53% in RcCS, SbCS, and CsCS, respectively, and

consequently mean weight diameter (MWD), mean geometric diameter (MGD) and aggregate

stability index (ASI) compared with those under CT. The tillage and crop rotations did not

significantly affect the majority of SOC and total N associated with aggregate size classes in all

depths in RcCS and CsCS but a recovery trend was noticed under NT in 0-5 cm depth. Although

SOC did not differ, aggregate-associated POXC under NT significantly increased in most size

classes in 0-5 cm depth in the three cropping systems. On average, and across all aggregate size

classes, NT accumulated SOC concentrations over CT by 11%, 7% and 6%, total N

concentrations by 3%, 11% and 15% and POXC concentrations by 18%, 20% and 15% for

RcCS, SbCS, and CsCS, respectively. The increasing trend was also observed in the subsoil

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layers. As a consequence of increased POXC, C management index (CMI) under NT was

promoted indicating the greater lability of SOC. The results of CP-MAS 13C NRM measurement

of the large macroaggregate in 0-5 cm showed that humic acid from soils under NT tended to

have higher proportions of aliphatic C than under CT while in reverse for aromatic C. In

addition, there were positive correlations between large macroaggregate-associated SOC and soil

aggregation indices (i.e., MWD, MGD, ASI) in 0-5 cm depth in the three cropping systems. In

conclusion, CT decreased the proportion of large macroaggregates, soil aggregation indices and

aggregate-associated SOC, total N and POXC while the adoption of NT showed a potential to

restore them back to the antecedent levels found under reference vegetation (RV).

5.1 Introduction

Soils can be either a sink for or a source of CO2 depending on land use and management

(Lal, 2003b, 2010). Changes in agricultural management practices to intensify crop production

profoundly affect soil organic C (SOC) dynamics (Chivenge et al., 2007; Lal, 1997; Six et al.,

2002). Soil organic matter (SOM) stabilization is controlled by three main mechanisms including

(a) chemically innate recalcitrance, (b) protection through interaction with minerals, and (c)

occlusion in aggregates (Mikutta et al., 2006). SOC and soil aggregation are the principal

determinants of soil productivity and sustainability and closely link one to another (Feller &

Beare, 1997). An increase in SOC enhances soil aggregation because it is considered one of the

major aggregating agents (Tisdall & Oades, 1982).

Soil aggregate stability plays a potential role in the ability of soil to sequester SOC and

might be used as a judicious strategy to mitigate the increase in atmospheric CO2 concentration.

Soil aggregation has a major influence on root development, C cycling and soil resistance to

erosion (Kay, 1998). The formation of stable soil aggregates is related to mineralogy, texture and

119

the quality and quantity of organic matter inputs (Feller & Beare, 1997). The proportions of soil

water stable aggregates often change rapidly when tillage practices and crop rotations are

modified (Angers et al., 1992). Aggregate-associated SOC provides strength and stability and

counters the impact of destructive forces and it is an important reservoir of soil C because of its

physical protection from microbial and enzymatic degradation (Bajracharya et al., 1997). A

positive relationship between SOC and aggregate stability was also reported (Dutartre et al.,

1993; Tisdall & Oades, 1982). Thus, maintaining high soil aggregate stability might lead to

increased SOC sequestration, an indicator of sustainable soil management practices. It has also

been known that iron and aluminum oxides and 1:1 clay minerals are the dominant binding

agents in oxide-rich soils in the tropics (Oades & Waters, 1991; Six et al., 2002). Amézketa

(1999) reviewed that the inorganic stabilizing agents (i.e., clays, polyvalent metal cations,

oxides, hydroxides of iron and aluminum, calcium and magnesium carbonates, gypsum) affect

soil aggregate formation and stabilization. They might offer protection to SOC against strong

structural alterations.

The practices of conventional tillage (CT) reduce the proportions of macroaggregates by

breaking down of soil aggregates (Zotarelli et al., 2007), thus hastening SOC oxidation through

stimulation of soil microbial biomass and activity (D. Guo et al., 2013; Six et al., 2004) and

affecting soil drying and wetting (Six et al., 2004). Unlike CT, no-till (NT) has less deleterious

effects on soil structure and maintains or sequesters SOC (Lal & Kimble, 1997). Enhanced soil

aggregation through NT can enhance the physical protection of SOC against losses due either to

mineralization or detachability and erosion (Feller & Beare, 1997). It is observed that soils under

NT have significantly higher aggregate stability, more macroaggregates-occluded

microaggregates and a greater SOC protection than under CT (Barreto et al., 2009; Denef et al.,

120

2004). Soil aggregate stability is a function of the liberation of aggregating agents, principally by

microorganisms, through the decomposition of organic residues (Cosentino et al., 2006). The

constant inputs of organic materials under NT generate a range of aggregating agents such as

fungal hyphae, microbial bio-products (Haynes & Francis, 1993) and root exudates

(Guggenberger et al., 1999). The role of plant derived polysaccharides in aggregate stability may

be also found in the fact that they may originate from plant detritus or from plant exudates

(Feller & Beare, 1997). The quantification of labile SOC fraction like permanganate oxidizable

C (POXC) might be crucial to indicate the presence of aggregating agents because of its positive

correlation to soil microbial activity including soil microbial biomass C (SMBC), soluble

carbohydrate C and total C (Weil et al., 2003).

Dynamics of large macro-aggregates (8-19 mm) may be a good indicator of potential C in

response to land use change and management due to their importance to protect recently

deposited labile SOC (Castro Filho et al., 2002). Tivet, Sá, Lal, Briedis, et al. (2013) emphasized

that continuous practices of CT negatively impacted distribution of water-stable aggregates and

loss of large macro-aggregates (8-19 mm) in a tropical red Latosol. The ability of NT practices in

rotation or association with cover crops to increase SOC sequestration varies among systems,

locations and soil depths. To manage individual soils effectively, the understanding of the

mechanisms that control SOC dynamics should be improved. Forest clearance to expand

agricultural land for the development of annual upland crops (i.e., rice, maize, cassava, soybean,

and mungbean) to satisfy the needs of growing population in Cambodia has exacerbated growing

concern over land degradation (Belfield et al., 2013; Hean, 2004; Poffenberger, 2009; UNDP,

2010) due to the CT practices that disrupt soil macroaggregates causing an increased release of

CO2 to the atmosphere. Considering the exponential increase of degraded soil cultivated with CT,

121

the application of NT has been introduced. However, the role of NT and residue retention in

aggregate formation is poorly documented in tropical agro-ecosystems in general and in

Cambodia in particular. The challenges to develop agricultural management practices to

sequester SOC through enhancement of soil aggregation and aggregate stability are necessary to

define sustainable crop production intensification in this country. The quantification of

aggregate-associated SOC and POXC distribution among aggregate size classes is fundamental

to a better understanding of the short-term effect of conservation agriculture (CA) on SOC

sequestration and the mechanism by which soils can sequester SOC. This will enable realistic

evaluation of the potential of crop rotation schemes for SOC sequestration. Therefore, this study

was conducted to quantify the changes in aggregate size distribution and levels of aggregate-

associated total SOC, total N and POXC after three-year CT and NT management practices in a

clayed soil in a tropical savanna agro-ecosystem.


Detailed descriptions of the site, experiments and biomass-C inputs are reported in

Chapter 3. Briefly, this study was executed in existing field experiments initiated in 2009 at

Bosknor Research Station, Kampong Cham, Cambodia (Latitude 12°12′30″N, longitude

105°19′7″E and 118 m elevation). Three distinct experiments comprised of (a) rice- (b) soybean-

and (c) cassava-based cropping systems (RcCS, SbCS, and CsCS, restectively). The plots were

arrayed in randomized complete block design with three replicates and four treatments

consisting of (a) CT in which main crops were planted in annual succession for rice and soybean

(i.e., mungbean/rice–CT-Rc, sesame/soybean–CT-Sb) and mono-cropping for cassava (CT-Cs);

(b) NT in which main crops were planted in a one year frequency pattern (NT1-Rc, NT1-Sb,

NT1-Cs); and (c) and (d) NT in which main crops were planted in bi-annual rotations with

122

maize, the two plots in these bi-annual rotations being NT2-Rc, NT3-Rc for rice, NT2-Sb, NT3-

Sb for soybean and NT2-Cs, NT3-Cs for cassava. The basal P fertilizer was applied by surface

banding with thermo phosphate (i.e., 16% P2O5, 31% CaO and 16% MgO), and fractioned top

dressing on main crops for N and K, using urea (46 % N) and potassium chloride (60 % K2O),

respectively. The total fertilizer input (2009-2011) was 161 kg ha-1 N, 144 kg ha-1 P2O5, 120 kg

ha-1 K2O5 for rice, 69 kg ha-1 N, 144 kg ha-1 P2O5, 180 kg ha-1 K2O5 for soybean, 230 kg ha-1 N,

144 kg ha-1 P2O5, 210 kg ha-1 K2O5 for cassava, and 230 kg ha-1 N, 144 kg ha-1 P2O5, 120 kg ha-1

K2O5 for maize. The aboveground biomass of main and cover crops were measured and the

belowground biomass-C inputs were estimated on the basis of the root to shoot ratio (RS ratio)

index. The details of cumulative and annual biomass-C inputs (2009-2011) in each cropping

system are presented in Table 5.1.

5.2.1 Water stable aggregate. Soil aggregate samples were taken in 70 cm × 70 cm pits

dug to 30 cm in November 2011 after three years of the experiment. Two clods for water-stable

aggregates were collected from each depth (0-5, 5-10 and 10-20 cm) in each plot in the three

cropping systems and reference vegetation (RV). Soon after sampling, each sample was wrapped

in plastic film to prevent moisture loss and excessive drying and to ease breakdown during

transportation from Cambodia to Brazil. Following capillary rewetting of each sample to field

moisture capacity, colds were softly broken along their natural cleavage planes before passing

through 19-mm mesh sieve (Castro Filho et al., 2002). The use of 19-mm sieve homogenizes

samples and does not underestimate the production of large macroaggregate under NT (Castro

Filho et al., 2002; Madari et al., 2005). Aggregate size classes were obtained by the wet sieving

procedure (Kemper & Rosenau, 1986) using a nest of seven sieves (8, 4, 2, 1, 0.5, 0.25 and 0.053

mm). The sieving procedure was simultaneously performed trice for each sample due to

123

variability in the distribution of soil aggregates. Prior to immersing in water, 60 g samples were

evenly spread on wetted filter paper on top of the 8-mm sieve and rewetted by capillary rise of

water for 10 min, and wet sieved at 30 oscillations min-1 for 15 min. At the end of vertical

oscillation, stable aggregates retrieved at each sieve were carefully backwashed into pre-weighed

containers, oven-dried at 40 ºC until a constant weight, and weighed. The following

classification was used herein: macroaggregates (2-4 to 8-19 mm), mesoaggregate (0.25-0.5 to 1-

2 mm) and microaggregate (0.053-0.25 mm).

5.2.2 Distribution of water stable aggregates and soil aggregation indices. The

proportion of water stable aggregate (WSA) was computed for each size class in relation to the

initial dry weight of the sample. Then, three aggregation indices were calculated as follow:

MWD = � ��

Where, MWD is the mean weight diameter (mm) of aggregates; xi is the mean diameter

of the classes (mm); wi is the proportion of each aggregate class in relation to the whole.

MGD = exp ��

�

Where, MGD is the mean geometric diameter (mm) of aggregates; wi is the weight of

aggregates (g) in a size class with an average diameter xi.

ASI = ! " × 100

Where, ASI is aggregate stability index; Mr is mass of resistant aggregates; and Mt is the

total mass of wet sieved soil.

5.2.3 Concentrations of soil organic C, total N and permanganate oxidizable C

associated with aggregate size classes. Sub-samples of each aggregate size class were finely

ground (< 150 mm) prior to determination of aggregate-associated SOC and total N by the dry

124

combustion method using an elemental CN analyzer (TruSpec CN, LECO, St. Joseph, USA).

Inorganic C in the studied soil was negligible so soil total C (TOC) was considered as SOC.

POXC concentrations in the seven size classes of soil aggregates were performed by the

method adapted from Tirol-Padre and Ladha (2004) and Culman et al. (2012). Briefly, 1.5 g of 2

mm-sieved aggregate soils was weighed into 15 mL polypropylene centrifuge tubes. The sample

was treated with 10 mL of a stock solution of KMnO4 (60 mM) and shaken on a vortex shaker

for 15 sec to suspend the soil in the stock solution. The tubes were horizontally shaken on a table

shaker at 200 rpm for 15 min at room temperature, and then centrifuged for 10 min at 4000 rpm.

2 mL of the supernatant was pipetted, transferred to a 125 mL Erlenmeyer flask and diluted with

100 mL deionized water. The absorbance of the solutions was determined at 565 nm using

Visible Spectrophotometer (SP-1105), and the amount of the oxidized organic C was calculated

from the KMnO4 consumed. The conversion of the absorbance to POXC concentration (g kg-1)

was done by using a standard calibration curve, based on the linear relationship between KMnO4

concentrations vs. absorbance at 565 nm. The concentration of POXC was computed as follow:

POXC (g kg-1) = [(mM blank – mM sample) × (125/2) × 10 × 9] / [1000 (mL L-1) × wt of sample

(g)]

where, mM blank and mM sample are the concentrations (mmol L-1) of KMnO4 in the

blank and sample, respectively, determined from the standard regression curve; 125/2 = the

dilution factor (mL mL-1); 10 = the volume (mL) of KMnO4 added to the soil sample; 9 = the

amount of C oxidized from every mole of KMnO4 (g mol-1 or mg mmol-1).

The C management index (CMI) in each soil aggregate size class was then calculated

following the mathematical procedures by Blair, Lefroy, and Lisle (1995) using POXC

concentrations. CMI provides a sensitive measure of the rate of change in soil C dynamics of a

125

given system relative to a more stable reference soil. The index was calculated for each of the

treatments using a reference sample value obtained from RV.

CMI = CPI × LI × 100

where, CMI is C management index; CPI is carbon pool index; LI is lability index.

The loss of C from a soil with a large C pool is of less consequence than the loss of the

same amount of C from a soil already depleted of C or which started with a smaller total C pool.

To account for this a C Pool size Index was computed as:

CPI = Sample total organic C (g kg-1) / Reference total organic C (g kg-1)

The loss of labile C is of greater consequence than the loss of non-labile C. The

reference vegetation soil was used as the reference. The labile C was considered as the portion of

SOC that was oxidized by KMnO4. To account for this, C Lability Index (LI) was computed as:

LI = Lability of C in sample soil / Lability of C in reference soil

Lability of C = POXC (g kg-1) / [SOC (g kg-1) – POXC (g kg-1)]

5.2.4 Humic acid extraction and solid-state 13C-Nuclear Magnetic Resonance

(NMR) Spectroscopy. Humic acid (HA) in three 8-19 mm aggregate-size class samples from

combination of three replicates of RV, CT and NT3 at 0-5 cm soil depth in SbCS was extracted

following the method of Swift (1996). Briefly, an oven-dried (40 ºC) and 2 mm-sieved aggregate

sample was used for H+ exchanging by 0.1 M HCl (pH 1–2), and overnight extraction with 0.1 M

NaOH (pH 12-13). The supernatant was recovered by centrifugation at 10,000 rpm (25 ºC) for 10

min, and the pH was immediately adjusted to 1.0–1.5 using 6M HCl (1:1 water:acid). The

residue was re-extracted and the supernatants were mixed. The acidified suspension was

centrifuged at 10,000 rpm (25 ºC) for 10 min and the sediment was re-dissolved with 0.1 M

KOH. Then, this solution was made 0.3 M with respect to KCl and the flocculated colloidal

126

particles were recovered by centrifugation at 10,000 rpm (25 ºC) for 10 min. The supernatant

was acidified to pH 1 by 6M HCl (1:1 acid:water) and precipitated HA was recovered by

centrifugation at 10, 000 rpm (25 ºC) for 10 min. The precipitated HA was re-suspended four

times with 0.1 M HCl /0.3 M HF solution for 16 hours. The extract (HA) was purified by

dialyzing with Milli-Q water for seven days and then lyophilized.

NMR measurements were performed on a Bruker Avance DRX 400 NMR spectrometer

(9.4 T) (Bruker Analytische Messtechnik GmbH, Rheinstetten, Germany). Cross Polarization–

Magic Angle Spinning (CP-MAS) pulse sequence was implemented using a standard MAS probe

4 mm at room temperature. HA samples were placed on a Kel-F rotor and were spun at 12 kHz.

The CP pulse sequence was accomplished with the contact time value of 1 ms and the time

acquisition value of 48 ms. During this time a SPINAL-64 pulse sequence was performed for

decoupling process between hydrogen and carbon nuclei (Lee & Goldburg, 1965). A recycle

time delay was 0.5 s and the number of scans was 50,000. The spectral window was related by

CH2 carbon (δiso = 43.5 ppm) of Glycine (Ye, Fu, Hu, Hou, & Ding, 1993).

5.2.5 Statistical analysis. The statistical analysis was performed using SAS 9.2 statistical

software. To compare significant effects of tillage and crop rotation treatments of each cropping

system at each depth, data were independently subjected to analysis of variance procedures with

randomized complete block design, and comparisons among treatment means were computed

based on least significant difference test (LSD) at the 0.05 probability level, unless otherwise

stated. Correlation coefficients between aggregate-associated SOC over size classes and soil

aggregation indices of mean values from the three replicates of CT and NT systems were

computed using the CORR procedure of SAS 9.2.

127

5.3 Results

5.3.1 Distribution of aggregate size classes and soil aggregate indices.

5.3.1.1 Rice-based cropping systems. Tillage and crop rotations did not have a significant

effect on macro- and mesoaggregate size distribution at the three depths but CT-Rc had

significantly higher amounts of microaggregates than NT-Rc (i.e., NT1-Rc, NT2-Rc, NT3-Rc) in

0-5 and 5-10 cm depths (Table 5.2). Although they did not differ, NT showed an increasing trend

of the greater proportion of large macroaggregate (8-19 mm) in 0-5 cm depth. NT-Rc averagely

had 23% more proportional distribution of macroaggregates than CT-Rc while soil under CT-Rc

tended to increase more meso- and microaggregates than under NT-Rc indicating the disruptive

effect of plowing. In general, there were no noticeable changes in soil aggregate size distribution

among treatments in 5-10 and 10-20 cm depths. When comparing with RV, cultivated soils had

significantly lower large macroaggregates (8-19 mm) at the two surface soil layers but greater

meso- and microaggregates were observed in cultivated soils. On average, and across all soil

depths, the proportion of 8-19 mm aggregate size fraction was 59%, 43% and 47% in RV, CT-

Rc, and NT-Rc, respectively. This proportion decreased with increasing soil depths in both RV

and cultivated soils.

The soil under RV was well aggregated and characterized by greater ASI compared with

CT-Rc and NT-Rc. Among the cultivated soils, the three NT-Rc treatments had greater ASI than

that of CT-Rc. In relation to large macroaggregates, soils under RV had significantly larger

MWD and MGD in 0-5 and 5-10 cm depths whereas those under NT-Rc showed increasing trend

compared with CT-Rc in 0-5 cm (Table 5.3). Similar values of the three aggregation indices

under treated soils were observed in 5-10 and 10-20 cm depths.

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5.3.1.2 Soybean-based cropping systems. Tillage and crop rotations significantly affected

distribution of large macro, meso- and microaggregates in 0-5 cm depth (Table 5.4). NT1-Sb,

NT2-Sb and NT3-Sb had 26%, 42%, and 50%, respectively, greater large macro-aggregates than

that of CT-Sb. This higher proportion of large macroaggregates under NT-Sb (i.e., NT1-Sb,

NT2-Sb, NT3-Sb) consequently reduced the proportions of meso- and microaggregates. In 5-10

and 10-20 cm depths, the increasing proportions of large macroaggregates under NT were also

observed while the distribution of other aggregate size classes among treatments was almost

constant. Soils under RV had significantly more large macroaggregates (8-19 mm) that those

under CT-Sb and NT1-Sb in 0-5 cm depth. Consequently, the soils under RV had lower amounts

of meso- and microaggregates compared with cultivated soils. The proportion of large

macroaggregates under the bi-annual rotations (NT2-Sb and NT3-Sb) did not differ from that

under RV. This recovery trend signified the importance of NT cropping systems in rotation and

association with diversified crop species in re-aggregating the soils. On average, and across all

soil depths, the proportions of large macroaggregates was 45% and 53% in CT-Sb and NT1-Sb,

respectively, and their levels ranged RV > NT3-Sb > NT2-Sb > NT1-Sb > CT-Sb.

Soils under RV and NT-Sb treatments had larger MWD and MGD compared with CT-Sb

in 0-5 cm depth (Table 5.5). They also well aggregated soils compared with CT-Sb as indicated

by higher ASI in the surface soil layer. In general, the increasing trend of the three aggregation

indices under NT-Sb was also observed in 5-10 and 10-2 cm depths and the significant effects on

these aggregation indices might be evident with time due to higher biomass-C inputs and less

physical disruption.

5.3.1.3 Cassava-based cropping systems. Adoption of NT management did not

significantly affect the proportion of aggregate distribution in all classes and depths except

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microaggregates in 5-10 and 10-20 cm depths (Table 5.6). On average, an increasing trend of

60% more proportion of large macroaggregates under bi-annual rotations (NT2-Cs and NT3-Cs)

over CT-Cs was observed in 0-5 cm depth. This increase in large macroaggregates consequently

decreased in meso- and microaggregates in NT-Cs (NT1-Cs, NT2-Cs, NT3-Cs) treatments. CT-

Cs significantly increased the proportions of microaggregates over the three NT-Cs treatments at

the two subsoil layers. The tendency of having more mesoaggregates in all soil depths and

microaggregates in the surface layer also appeared. The soil under RV had more large

macroaggregates than CT-Cs and NT1-Cs, resulting in a lower proportion of meso- and

microaggregates. The increasing proportions of large macroaggregates by 28% and 29% under

NT-Cs treatments compared with CT-Cs were also observed in 5-10 and 10-20 cm depths,

respectively. On average, and across all soil depths, the proportion of 8-19 mm size fraction

decreased from 59% in soil under RV to 34% and 47% under CT-Cs and NT-Cs, respectively.

Soils under RV had 29% larger MGD than cultivated soils, and 73% and 33% larger

MWD than CT-Cs and NT1-Cs, respectively, in 0-5 cm depth. However, MWD and MGD did

not differ among RV and treatments in 5-10 and 10-20 cm depths (Table 5.7). Soil under RV

aggregated more than CT-Cs as characterized by higher ASI. Even no significant increase in soil

aggregation after three-year NT practices compared with CT-Cs, an increasing trend of better

aggregation was observed under NT-Cs in all soil depths. On average, and across all soil depths,

NT-Cs treatments had 5% more ASI than CT-Cs. The significant improvement of aggregation

indices might be evident with time.

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5.3.2 Aggregate-associated soil organic C, total N and permanganate oxidizable C,

and C management index.

5.3.2.1 Rice-based cropping systems. SOC and total N concentrations associated with

aggregates were nearly constant among size classes in all depths except the microaggregates

which had higher concentrations than others in 0-5 and 5-10 cm depths. In general, tillage and

crop rotations did not significantly affect SOC and N concentrations in all aggregate size classes

(Tables 5.8 and 5.9). Even no significant differences, higher values of SOC concentrations were

observed in soil under NT-Rc compared with CT-Rc in 0-5 and 5-10 cm depths. The per cent

increase in SOC concentrations in the surface layer was greater in the subsoil layer. On average,

NT-Rc increased more SOC concentrations than that of CT-Rc by 10%, 9% and 15% in 0-5 cm

and by 7%, 6% and 5% in 5-10 cm in macro-, meso-, and microaggregate size classes,

respectively. Results of total N concentrations were partially consistent to SOC, which NT-Rc

tended to increase more aggregate-associated total N. Although SOC and total N associated with

aggregates did not differ, POXC showed a significant difference in all size classes in 0-5 cm

depth (Table 5.10). On average, and across all size classes, the bi-annual rotations (NT2-Rc and

NT3-Rc) significantly increased 24% POXC greater than that of CT-Rc. NT1-Rc showed greater

POXC concentration in 8-19, 4-8 and 1-2 mm aggregate fractions than CT-Rc. The increasing

trend of NT-Rc over CT-Rc was also observed in 5-10 and 10-20 cm depths. When comparing to

RV, aggregate-associated SOC, total N and POXC concentrations were greater in large

macroaggregates and microaggregates in RV soil compared with those in cultivated soils in 0-5

and 5-10 cm depths. Aggregate-associated SOC, total N and POXC concentrations in all

aggregate size classes of each depth and treatment were nearly constant.

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Based on the results of POXC concentrations, the bi-annual rotations showed greater

CMI compared with CT-Rc in all aggregate size classes in 0-5 cm depth and NT1-Rc also had

higher CMI in 8-19 and 1-2 mm aggregate fractions (Table 5.11). On average, and across all

aggregate size classes, the bi-annual rotation treatments had CMI by 19% greater than CT-Rc.

Although they did not differ in the two subsoil layers except 1-2 mm fraction in 10-20 cm depth,

the three NT-Rc treatments tended to promote CMI values. On average, and across all aggregate

size classes, NT-Rc had higher CMI than that of CT-Rc by 9% and 16% in 5-10 and 10-20 cm

depths, respectively.

5.3.2.2 Soybean-based cropping systems. Significant effects of tillage and crop rotations

were observed for SOC associated with large macroaggregates in 0-5 and 5-10 cm depths, and 4-

8 mm fractions in 0-5 cm depth (Table 5.12). NT3-Sb significantly increased SOC

concentrations by 13% and 11% at 0-5 and 5-10 cm depths, respectively. On average, NT-Sb

quantitatively increased SOC concentrations by 9%, 4% and 7% in 0-5 cm, by 10%, 12% and

14% at 5-10 cm and by 5%, 8% and 10% at 10-20 cm in macro-, meso-, and microaggregate size

classes, respectively, compared with CT-Sb. Similarly, significant effects on aggregate-

associated total N were detected in 0.053-0.25 mm in 0-5 cm, 8-19 mm in 5-10 cm and 8-19 and

4-8 mm aggregate fractions in 10-20 cm depth (Table 5.13). The per cent increase in total N

concentrations under NT-Sb was greater than SOC. On average, and across all aggregate size

classes, NT-Sb soils had more total N concentrations by 12%, 19% and 11% compared with CT-

Sb in 0-5, 5-10, and 10-20 cm depths, respectively. Similar to SOC, POXC concentrations

differed among treated soils in 8-19 and 4-8 mm aggregate size classes in 0-5 cm depth (Table

5.14). The three NT-Sb treatments averagely accumulated 25% and 22% greater POXC

concentrations than those of CT-Sb in 8-19 and 4-8 mm fractions. On average, and across all

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aggregate size classes, NT-Sb soils accumulated more aggregate-associated POXC by 20%, 9%

and 17% compared with CT-Sb in 0-5, 5-10, and 10-20 soil depths, respectively. Although, they

did not differ in the two subsoil layers, significant differences might be evident with time since

higher biomass-C inputs under NT-Sb were continuously added to the soils. In general, RV soils

had greater aggregate-associated SOC, total N and POXC concentrations in large macro- and

microaggregate size classes in 0-5 cm depth compared with those of cultivated soils. On average,

RV had greater SOC by 31% and 60%, total N by 43% and 82%, and POXC by 25% and 59% in

large macro- and microaggregates, respectively.

In relation to POXC concentrations, NT-Sb averagely increased CMI by 26% and 24% in

8-19 and 4-8 mm aggregate size classes, respectively, in 0-5 cm depth (Table 5.15). CMI values

also tended to be promoted by the adoption of NT practices in rotation or association with

diversified cover crop species in the subsoil layers. On average, and across all aggregate size

classes, NT-Sb had higher CMI values than those of CT-Sb by 22%, 8%, and 17% in 0-5, 5-10,

and 10-20 cm depths, respectively. The bi-annual rotation treatments were likely to promote

more CMI than NT1-Sb.

5.3.2.3 Cassava-based cropping systems. Similar to RcCS, tillage and crop rotations did

not significantly affect the concentrations of SOC and total N in all aggregate size classes and

depths (Tables 5.16 and 5.17). Although they did not differ, soils under NT practices tended to

increase more SOC and total N concentrations in all depths. On average, soils under NT-Cs

accumulated higher SOC than CT-Cs by 6%, 5% and 5% at 0-5 cm, by 4%, 3% and 3% at 5-10

cm, and by 7%, 13% and 16% at 10-20 cm depths in macro-, meso-, and microaggregate size

classes, respectively. Total N concentrations resulted in similar increasing trend. On average, and

across all size classes, soils under NT-Cs had 15%, 7%, and 6% more total N concentrations than

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those under CT-Cs in 0-5, 5-10, and 10-20 cm depths, respectively. Unlike SOC and total N,

POXC concentrations in most aggregate size classes in 0-5 cm depth were increased in NT-Cs

(Table 5.18) while bi-annual rotations showed a greater increasing trend compared with NT1-Cs.

On average, and across all size classes, bi-annual rotation treatments had 20% greater POXC

concentrations than that of CT-Cs. The increasing trend was also observed in the two subsoil

layers, in which NT-Cs had 16% and 27% more POXC concentrations compared with those of

CT-Cs in 5-10 and 10-20 cm depths. The concentrations of SOC, total N and POXC were nearly

constant in all aggregate size classes. In general, soils under RV accumulated greater SOC, total

N and POXC concentrations than cultivated soils in large macroaggregates in 0-5 cm depth and

in microaggregates in 0-5 and 5-10 cm depths.

Similar to RcCS, bi-annual rotations significantly increased CMI in all aggregate size

classes, except 2-4 mm, in 0-5 cm depth (Table 5.19). On average, and across all size classes, the

bi-annual rotation treatments promoted 22% greater CMI than that of CT-Cs. Although

significant differences did not exist, NT1-Cs promoted 5% more CMI values than CT-Cs. The

increasing trend of CMI under NT practices was also observed in the two subsoil layers. On

average, and across all aggregate size classes, NT-Cs had higher CMI than that of CT-Cs by 17%

and 29% in 5-10 and 10-20 cm depths, respectively.

5.3.3 Relations between SOC associated with aggregate size classes and soil

aggregate indices. Table 5.20 showed significantly positive correlations (P ≤ 0.05) between

SOC associated with large and smallest macroaggregates (8-19 and 2-4 mm, respectively) and

the three soil aggregation indices in 0-5 cm depth in RcCS. Similarly, the three soil aggregation

indices in SbCS positively correlated to large macroaggregate-associated SOC (Table 5.21). In

addition, MWD and MGD of the smallest macroaggregate in 0-5 cm depth and microaggregates

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in 5-10 cm depth were also positively correlated to their associated SOC. CsCS showed positive

correlations between soil aggregation indices and SOC associated with most aggregate size

classes in 0-5 cm depth, and between ASI and microaggregate-associated SOC in 10-20 cm

depth (Table 5.22). SOC associated with large and second large macroaggregates (8-19 and 4-8

mm, respectively) positively correlated (P ≤ 0.01) to the three soil aggregation indices except

MGD of second large macroaggregates (P ≤ 0.05). The positive correlations (P ≤ 0.05) were also

observed in the smallest macro- and mesoaggregates except MGD of the 1-2 mm aggregate size

class. The presence of positive correlations between soil aggregation indices and the SOC

associated with the large macroaggregate size class in the three cropping systems could be

evident that the increased proportions of large macroaggregates after the adoption of NT systems

in rotation or association with diversified crop species could partially restore SOC in the surface

soil layer and potentially in the subsurface layers.

5.3.4 Solid-state 13C-Nuclear Magnetic Resonance spectroscopy of humic acid. The

HA 13C CP-MAS NMR spectra of 8-19 mm soil aggregate size class of RV, CT and NT in 0-5

cm depth are shown in Figure 5.1 which represented the intensities of 13C signals of HAs. The

HA signals are divided into seven main chemical shift ranges: 0-45, 45-65, 65-90, 90-110, 110-

143, 143-160, and 160-188 ppm. In the aliphatic region (0-110 ppm) is dominated by the peaks

at 25 and 30 ppm for alkyl C, 56 ppm for methoxyl C which overlaps with intensity derived from

N-alkyl, 71 ppm for O-alkyl C and 102 ppm for anomeric C. Apart from this, the aromatic (110-

143 ppm) and phenolic (143-160 ppm) regions are dominated by the peaks at 129 and 151 ppm

coming from aromatic C and the phenolic C, respectively. The presence of carboxyl C with a

maximum peak at 173 ppm was also noted in the carboxylic region (160-188 ppm). There were

no major differences in term of presence of specific peaks in the three land uses. However, the

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signal intensity contribution of the chemical groups can be obtained. HA from RV showed

higher signal intensities of aliphatic and carboxylic chemical groups than those from CT and NT.

In general, HA from NT tended to have higher signal intensities of the aliphatic group,

particularly O-alkyl C than that from CT. In contrast, higher signal intensities of the aromatic

group in HA from CT than those from NT and RV were detected. The levels of the signal

intensities of the aromatic group ranged CT > NT > RV. This characteristic indicates that

aliphatic and carboxylic components were naturally transformed into aromatic components as a

result of land manipulation.

5.4 Discussion

5.4.1 Effect of conservation agriculture on size distribution of water stable

aggregates and soil aggregation indices. Soil aggregate stability is dependent on texture, clay

mineralogy, exchangeable ions, aluminum and iron oxides, SOC concentration and microbial

activities (Bronick & Lal, 2005; Kay, 1998) and changes in agricultural management practices

(i.e., tillage practices and crop rotations) rapidly influence the proportional distribution of soil

water stable aggregates (Angers et al., 1992). Castro Filho et al. (2002) suggested the use of 19

mm sieve to homogenize soil samples before wet-sieving because the use of smaller sizes of

sieves underestimates the actual ability of NT to form large stable aggregates. In the present

study, NT systems in RcCS, SbCS and CsCS increased the proportion of large macroaggregates

compared with CT systems. Stable large macroaggregates (8-19 mm) generally dominated

aggregate distribution in soils under both CT and NT practices. Previous work on comparison of

CT and NT effects concentrated on the proportions of stable macroaggregates with smaller than

8 mm size classes or even smaller but very few studies have compared 8-19 mm size classes. The

high proportions of stable large macroaggregates in clayed soils were also reported in the other

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studies in the tropical and subtropical climates (Madari et al., 2005; Tivet, Sá, Lal, Briedis, et al.,

2013). The increase in the large macroaggregate proportion under NT consequently led to lower

meso- and microaggregate proportions. Since CT contained more proportions of meso- and

microaggregates than did NT, there could be a greater risk of aggregate-associated SOC, total N

and POXC from CT soils.

Soil aggregation is one of important mechanisms to protect and consequently to sequester

SOC (Feller & Beare, 1997; Lützow et al., 2006). The process of soil aggregation under NT

systems with continuous biomass-C inputs depends on an increase in aggregating agents such as

fungal hyphae, microbial bi-products (Haynes & Francis, 1993) and root exudates

(Guggenberger et al., 1999) and a decrease in physical disruption of macroaggregates (Barto,

Alt, Oelmann, Wilcke, & Rillig, 2010). Although NT practices did not lead to a significant

increase in MWD, MGD and ASI compared with those of CT in RcCS and CsCS in this study

except ASI in RcCS, soils under NT consistently showed higher proportion of large

macroaggregates leading to larger MWD and MGD and higher ASI in the surface soil layer

especially. These increasing trends might be also evident in the subsurface soil layers over time

since it was already apparent in SbCS. The possible contributing factor could be the continuous

greater supply of biomass-C through high cropping intensity systems providing aggregate

binding agents and the absence of physical disruption in the NT systems that might influence the

increase in soil macro-aggregation. Additionally, the increase in roots from diversified crop

species under NT are also involved in macroaggregate stabilization (Tisdall & Oades, 1982). It

was obvious that SOC concentrations associated with large macroaggregates positively

correlated with the three soil aggregation indices in the surface soil layer in the three cropping

systems. Tivet, Sá, Lal, Briedis, et al. (2013) found a significant increase in large

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macroaggregate fractions and labile SOC under eight-year NT systems with diversified crop

species rotations and a positive correlation between soil aggregation indices (i.e., MWD, MGD,

ASI) and labile fractions of SOC. Hok et al. (under review) evaluated the changes in total SOC

concentrations in the same experiment. They reported that soils under NT had higher SOC than

those of CT in SbCS and CsCS but did not find a significant difference in RcCS. This could

partially be contributed by this higher large macro-aggregation in soils under NT.

Macro- and mesoaggregate fractions might also positively correlate with clay plus fine

silts. In general, there is less effect of SOC on soil aggregation in highly weathered soils of the

tropics because iron and aluminum oxides and 1:1 clay minerals are the dominant binding agents

in oxide-rich soils (Oades & Waters, 1991; Six et al., 2002). Hok et al. (under review) reported

that this studied soil was Oxisols and dominated by kaolinite, and the clay plus silt contents in all

depths were nearly constant in all treatments and depths and represented ~ 99%. Amézketa

(1999) reviewed that the inorganic stabilizing agents including clays, polyvalent metal cations

such as Ca2+, Fe3+, and Al3+, oxides and hydroxides of Fe and Al, calcium and magnesium

carbonates and gypsum positively affect soil aggregate formation and stabilization. Thus, these

major factors mentioned above could partially contribute to the slow effects of short-term NT

practices with high and diversified biomass-C inputs in RcCS and CsCS on enhancement of

macroaggregate formation and aggregation indices over CT that could lead to a significant

increase in the present study. It partially corroborates with Tivet, Sá, Lal, Milori, et al. (2013)

who concluded that main aggregating agents in Oxisols are not only clay and oxides contents but

also the constant (rizho) deposition of organic matter, which maintains the binding effect and

increases the proportion of water stable aggregates based on the results of SOC and HLIF

distribution among aggregate size classes in their study. Thus, the significant changes might be

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detected with time due to the continuous provision of biomass-C as aggregate binding agents

from crop residues under NT.

5.4.2 Effect of conservation agriculture on aggregate-associated SOC, total N and

POXC. The soil stability can be positively related to the proportions of large macroaggregates,

normally containing most of C in the soil (Six et al., 2004). SOC sequestration increase in

tropical soils is influenced by NT cropping systems in rotation or association with cover crops

due to the absence of physical soil disruption and continuous biomass-C inputs (Bayer, Martin-

Neto, et al., 2006; Neto et al., 2010; Sá et al., 2013). The formation of macroaggregates increases

SOC sequestration because SOC can be protected by occlusion in soil aggregates (Lützow et al.,

2006; Mikutta et al., 2006; Six et al., 2000; Tivet, Sá, Lal, Briedis, et al., 2013). The positive

correlation between SOC concentrations and large macroaggregate (8-19 mm) formation in the

tropical and subtropical climate were previously reported (Briedis, Sá, Caires, Navarro, et al.,

2012; Madari et al., 2005; Tivet, Sá, Lal, Briedis, et al., 2013). Thus, the enhancement of stable

large macroaggregates may lead to an increase in the ability of soil to sequester SOC. In the

present study, accumulated SOC within large macroaggregates under CT decreased mainly in the

0-5 cm depth compared with NT in the three cropping systems. This was probably due to the

presence of CT that reduced the proportion of large macroaggregates (8-19 mm) which may

explain lower SOC concentrations. The decrease was also observed in other macro- and

mesoaggregate fractions. The continuous CT destroys soil aggregates (Zotarelli et al., 2007) and

consequently increases soil aeration that stimulates soil microbial biomass and activity (D. Guo

et al., 2013), thus hastening SOC oxidation (Green et al., 2007; Jastrow et al., 1996; Reicosky et

al., 1995), which resulted in the decreased SOC. In the majority of smaller aggregate size classes,

NT treatments also resulted in higher aggregate-associated SOC in the three cropping systems.

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This could be a partial consequence of lower total SOC in bulk soils under CT as Hok et al.

(under review) reported that NT practices had higher SOC than those of CT in SbCS and CsCS

but not RcCS in the same experiment.

The intensified cropping sequence under NT provided continuous biomass-C inputs to

maintain the C flow in the soil that could enhance the process of soil aggregation and C

transformations. It is quite consistent that SOC associated with macroaggregates were slightly

higher than those with mesoaggregates in the three cropping systems. This behavior could be

probably explained by the concept of aggregate hierarchy (Oades, 1984; Tisdall & Oades, 1982)

which stated that organic matter within large macroaggregates tends to be higher than that in

smaller aggregates because fresh organic matter is the precursor in macroaggregate formation.

As a result of high accumulation of crop residues in the soil surface under NT, the fresh crop

residues are easily accessible by soil microorganisms for metabolism process and sometimes

even more than their capacity to metabolize them, which leads to a great input of metabolizable

organic compounds into SOM (Bayer et al., 2002). Examination of CP-MAS 13C NRM data of

studied soils from the large macroaggregate in the surface layer illustrated that HA from NT soil

tended to have higher signal intensities of aliphatic C (0-110 ppm) than that from CT, especially

O-alkyl C which was derived from crop residues returned to the soil. In contrast, SOC associated

with microaggregates was likely to be higher than that with mesoaggregates but comparable to

macroaggregates. Microaggregates play an important role in SOC sequestration (Jastrow et al.,

1996). This microaggregate-associated SOC is more stable than that in macroaggregates because

they have more reactive surface area due to an increase in clay and sesquioxides and are

physically protected with microaggregates (Barthès et al., 2008; Feller & Beare, 1997). The

formation of microaggregate within macroaggregates is crucial to SOC storage and stabilization

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(Six et al., 2000; Tivet, Sá, Lal, Briedis, et al., 2013) due to the physical protection within

macroaggregates.

The continuous supply of high aboveground crop residues and root biomass via the

incorporation of deep rooting forage species into crop rotations supplied greater fresh C in NT

than CT systems leading to increased microbial activities (Lienhard et al., 2013). Consequently,

SOC decomposition rates increase. The microbes are also able to decompose the native C or

recalcitrant C compounds with their enzymes using fresh C as a source of energy (Fontaine et al.,

2007). During the six-month dry season in this study, no cover crops were planted in CT plots

while the permanent organic soil cover was maintained in the NT plots. The latter might

consequently increase the humification process of native C attaining more advanced stages, with

a relative decrease of the concentration of more recalcitrant organic compounds. The CP-MAS

13C NRM data of the studied soils from large macroaggregate in the surface layer revealed that

HA from CT had higher signal intensities of aromatic C (110-143 ppm) than those of NT and RV

in 0-5 cm soil layer. This also contributes to the finding of slow effect of intensified NT crop

rotations with diverse cover crop species on total SOC compared with CT as reported by Hok et

al. (under review) at the same experiment. When comparing with RV, it is evident that HA

extracted from cultivated soils had higher proportions of aromatic C than RV. The order levels of

aromatic C were CT > NT > RV. This finding was similar to those reported by González Pérez et

al. (2004). They found that HAs from the non-cultivated and NT/maize-cajanus soils (i.e., bulk

soils) of subtropical Oxisols had less concentration of aromatic C than that from CT, shown by

CP-MAS 13C NRM, electron paramagnetic resonance (EPR), and Fourier transform infrared

(FTIR). Similarly, Mahieu, Randall, and Powlson (1999) also reported that HAs from cultivated

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soils had higher concentration of aromatic C than those from non-cultivated soils determined by

CP-MAS 13C NRM.

It has been known that grass and legume cover crops act as a source of supplement N to

the soil (Wagger et al., 1998) so soil N can be increased with an increase in the amount of

residue returned to the soil (Ghimire et al., 2012). In the present study, aggregate-associated total

N in the majority of aggregate size classes under NT in all depths in the three cropping systems

showed an increasing trend while its significant increase in some aggregate size classes were

already detected. This result is reflective of the differing amounts of above- and belowground

crop biomass inputs and types of crop residues returned to the soil leading to increased total N

concentrations. Consequently, the soil aggregation under NT is promoted due to increased

microbial biomass and activities which in turn synthesizes polymers that act as aggregating

agents (Jastrow et al., 1996). Tivet, Sá, Lal, Briedis, et al. (2013) reported that the inclusion of

grass and legumes as cover/relay crops in NT crop rotations highly produced monosaccharide

(i.e., arabinose and xylose) that could directly or indirectly enhance soil aggregation through

their influence on soil microbes. The absence of soil physical disruption under NT might also

contribute to the increased N because the mechanical soil disturbance by CT operations might

allow N released from crop residues to be mineralized more rapidly due to lack of physical

protection. Our results showed a higher accumulation of aggregate-associated total N in most

aggregate size classes under NT compared with those under CT, which coincided with Six et al.

(2002) who reviewed that N is protected against mineralization within aggregates. The increased

N mineralization in tropical and temperate soils exists when the aggregate structure is disrupted.

The N stabilization within aggregates is partly related to the decrease in oxygen concentration in

the center of soil aggregates. The N distribution patterns in aggregate size classes were similar

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across the three cropping systems, with macro and microaggregates having higher concentrations

than mesoaggregates. These results indicate the importance of stable macroaggregates and total

N associated with microaggregates in N retention. Even higher N concentrations in

microaggregates, they do not reflect the greater N stocks in the soils based on a mass basis. In

general, the proportions of macroaggregates were very much greater than those of

microaggregates.

POXC is a labile SOC pool that is sensitive to short-term land use changes and correlates

with SMBC, soluble carbohydrate C and total C (Melero et al., 2009; Weil et al., 2003). The

changes in labile SOC pool can be served as an indicator of future changes in total SOC. Soil

aggregate stability positively correlated to residue restitution and fungal and bacterial densities

under NT systems (Lienhard et al., 2013), and POXC (Stine & Weil, 2002). The greater biomass-

C inputs that increased POXC under NT systems maybe consequently enhance soil

macroaggregate formation which may protect SOC. Although the continuity of C supply through

crop residues under NT did not lead to a significant increase in aggregate-associated SOC

compared with CT after three years in RcCS and CsCS, it is obvious that NT in the three

cropping systems significantly increased concentrations of POXC associated with the large

macroaggregates and also with most other size classes in RcCS and CsCS compared with CT in

0-5 cm depth. The possible explanation for the high POXC concentrations under NT was

probably due to the higher fresh organic matter inputs from the diverse crop residues included in

the crop associations or rotations as shown in the CP-MAS 13C NRM data in Fig. 1 that indicates

higher signal intensities of aliphatic C under NT than that under CT, the absence of soil physical

disruption that exposed young SOC to microbial oxidation and the increased proportions of large

macroaggregates that could be formed around the fresh C obtained from the crop residue

143

returned to the soil. Tivet, Sá, Lal, Briedis, et al. (2013) reported the recently deposited labile

SOC (i.e., particulate organic C, hot-water extractable C and total polysaccharides) can be

potentially protected in large macroaggregates (8-19 mm) and the labile SOC fractions positively

correlated to SOC concentrations in aggregate size classes. The consistent effect of NT in

rotation or association with diversified cover crop species on POXC in this study suggests that

this labile SOC pool may be a good indicator to assess SOC dynamics within aggregates in short-

term soil management practices and to estimate long-term trends. These increased POXC

concentrations within large macroaggregates under NT led to greater CMI compared with CT

due to an increase in the lability of SOC in the surface soils in the three cropping systems. These

findings suggest that labile SOC is restored faster than SOC associated with aggregates

especially in the large macroaggregates, indicating the potential of NT systems to rehabilitate the

soil quality and to sequester SOC through enhancement of macro-aggregation that can stabilize

SOC within macroaggregate-occluded microaggregates. The increasing trend of aggregate-

associated POXC accumulation under NT also showed the two subsurface soil layers. This

probably resulted from the rate of biomass-C inputs from crop residues retained on the soil

surface, which create a positive C budget, accentuate C transformation and support a continuous

flow of biomass which releases organic compounds (Sá et al., 2013), the incorporation of deep-

rooting cover crops such as Congo grass, sorghum, millet, stylo and sunhemp that could provide

root biomass and exudates, and less soil aggregate disruption leading to decreased oxidation of

this labile SOC.

5.5 Conclusions

Conversion of RV to cultivated land dramatically influenced the distribution of aggregate

size classes, soil aggregation indices and aggregate-associated SOC, total N and POXC in the

144

two surface layers. The aggregate stability depends primarily on the formation of large

macroaggregates (8-19 mm) which dominated aggregate size distribution with relatively higher

proportions under RV and NT than CT. The soil aggregation indices positively correlated with

the large macroaggregate-associated SOC. Large macroaggregates which plays an important role

in storage and stabilization of SOC, total N and POXC within macroaggregate-occluded

microaggregates are disrupted by the continuous CT. Reduction in physical disruption combined

with crop residue retention in the soil surface within three years of this study significantly

increased the SOC retained in the large macroaggregates of the top soil in CsCS and showed a

recovery trend in RcCS and SbCS due to greater aggregate stability. The labile SOC (i.e.,

POXC) was more sensitive to this short-term change of agricultural management practices than

total SOC resulting in a significant increase in its concentration in the majority of aggregate size

classes under NT compared with CT in the soil surface layer and consequently promoting CMI.

The results of CP-MAS 13C NRM measurement suggest that the continuous biomass-C inputs via

crop residues under NT tended to increase the proportions of aliphatic C than under CT while in

reverse for aromatic C. Among NT systems, the bi-annual crop rotations in the three cropping

systems tended to be more effective than one year frequency pattern in enhancing large macro-

aggregation and restoring the concentrations of SOC, total N and POXC associated with large

macroaggregates. Thus, they might be served as the appropriate crop rotation scheme to

maximize SOC, total N and POXC retention in the surface soil and a potential restoration in the

subsurface soil layers in a longer period as a result of continuity of high biomass-C inputs.

145

Figure 5.1. CP/MAS 13C NMR spectra of humic acids extracted from large soil

macroaggregates (8-19 mm) under reference vegetation (RV), conventional tillage (CT) and no-

till (NT) in 0-5 cm depth.

Chemical shift (ppm)

146

Table 5.1

Land use, crop sequence, and C input in the three-year experiment period (2009-2011)

C input (Mg ha-1)




Mb/Rc – Mb/Rc – Mb/Rc Mt/Rc+St – Mt+Cr/Rc+St – St(2010)¥/Rc+St Mt/Rc+St–Mt+Cr+St (2009)/Mz+St–Mt+Cr+St (2010)/Rc+St Mt/Mz+St – Mt+Cr+St (2009)/Rc+St – St (2010)/Mz+St

6.27 18.83 16.64 16.65

2.09 6.28 5.55 5.55



Se/Sb – Se/Sb – Se/Sb Mt/Sb+Brz – Brz(2009)/Sb+St – Mt/Sb+St+Sg Mt+/Sb+St – Mt+Cr+St (2009)/Mz+St – Mt/Sb+St Mt/Mz+Brz – Mt/Sb+St – Mt+Cr/Mz+St

4.92 18.42 21.96 21.87

1.64 6.14 7.32 7.29



Cs – Cs – Cs Cs+St – Cs+St – Cs+St Cs+St – Mt+St (2009)/Mz+St – St (2010)/Cs+St Mt/Mz+St – Cs+St – Mt+Cr+St (2010)/Mz+St

4.08 12.42 13.73 15.35

1.36 4.14 4.58 5.12


147

Table 5.2

Distribution of aggregate size classes (g soil in aggregate fraction kg-1 soil) in reference

vegetation (RV) and different treatments in rice-based cropping systems

Depth Aggregate size classes (mm)

(cm) Land use 8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5

5–10

10–20

RVa

CT-Rcb

NT1-Rc

NT2-Rc

NT3-Rc

RV

CT-Rc

NT1-Rc

NT2-Rc

NT3-Rc

RV

CT-Rc

NT1-Rc

NT2-Rc

NT3-Rc

718 A

478 Cns

561 BC

607 AB

601 B

619 A

474 Bns

463 B

478 B

454 B

424 ns

325

319

369

321

99 ns

104

100

107

94

119 ns

123

113

101

112

129 ns

113

116

136

141

64 ns

76

80

78

71

98 ns

81

99

105

110

136 ns

131

140

141

149

34 B

83 Ans

78 A

68 A

66 A

58 B

88 ABns

111 A

109 A

106 A

113 ns

150

155

132

128

25 C

105 Ans

79 AB

59 BC

72 AB

44 B

90 Ans

105 A

99 A

96 A

97 ns

134

132

99

121

22 C

60 Ans

46 AB

32 BC

42 B

27 B

61 Ans

50 A

48 A

56 A

53 ns

65

67

58

71

16 C

49 Aa

33 Bb

23 BCb

31 Bb

17 C

40 Aa

34 Bb

34 Bb

42 Aa

32 ns

50

42

41

48

Note. RV: reference vegetation; CT: conventional tillage; NT: no-till; Rc: rice; a Comparison between tillage systems CT-Rc, NT1-Rc, NT2-Rc, NT3-Rc and RV; Uppercase letters within the same column in each aggregate size class of each depth in each cropping system indicate the difference among RV and tillage treatments at P ≤ 0.05 by LSD. b Comparison among tillage systems CT-Rc, NT1-Rc, NT2-Rc and NT3-Rc; Lowercase letters within the same column indicate difference between tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

148

Table 5.3

Mean weight diameter (MWD), mean geometric diameter (MGD) and aggregate stability index

(ASI) in reference vegetation (RV) and different treatments in rice-based cropping systems

Depth (cm)

0-5 5-10 10-20

Land Use

MWD (mm)

MGD (mm)

ASI (%)

MWD (mm)

MGD (mm)

ASI (%)

MWD(mm)

MGD (mm)

ASI (%)

RVa

CT-Rcb

NT1-Rc

NT2-Rc

NT3-Rc

10.56 A

7.54 Cns

8.61 BC

9.24 B

9.07 B

2.46 A

1.82 Cns

2.04 BC

2.19 B

2.12 B

98.2 A

92.8 Cb

95.5 Ba

97.1 ABa

96.0 Ba

9.49 A

7.62 Bns

7.50 B

7.63 B

7.39 B

2.27 A

1.86 Bns

1.87 B

1.89 B

1.83 B

98.0 A

94.1 Bns

94.7 B

94.9 B

93.9 B

7.17 ns

5.82

5.78

6.52

5.95

1.84 ns

1.61

1.61

1.73

1.63

95.3 ns

90.4

92.4

93.7

92.3

RV: reference vegetation; CT: conventional tillage; NT: no-till; Rc: rice; a Comparison between tillage systems CT, NT1, NT2, NT3 and RV; Uppercase letters within the same column in each aggregate size class of each depth in each cropping system indicate the difference among RV and tillage treatments at P ≤ 0.05 by LSD. b Comparison among tillage systems CT, NT1, NT2 and NT3; Lowercase letters within the same column indicate difference between tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

149

Table 5.4


vegetation (RV) and different treatments in soybean-based cropping systems

Depth (cm)

Land use

Aggregate size classes (mm)

8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5

5–10

10–20

RVa

CT-Sbb

NT1-Sb

NT2-Sb

NT3-Sb

RV

CT-Sb

NT1-Sb

NT2-Sb

NT3-Sb

RV

CT-Sb

NT1-Sb

NT2-Sb

NT3-Sb

718 A

464 Cc

584 Bb

659 ABab

694 Aa

619 ns

491

510

488

536

424 ns

384

438

401

472

99 ns

79

84

79

77

119 A

101 Bns

100 B

90 B

97 B

129 ns

118

116

110

131

64 ns

69

78

67

58

98 ns

88

92

98

86

136 ns

125

114

135

104

34 D

103 Aa

74 Bb

60 BCbc

48 CDc

58 ns

98

102

98

87

113 ns

124

109

124

99

25 C

111 Aa

72 Bb

57 Bb

46 BCb

44 ns

99

95

100

90

97 ns

109

105

107

88

22 C

74 Aa

52 Bab

32 BCbc

29 Cc

27 ns

50

48

65

47

53 ns

72

58

68

49

16 C

60 Aa

35 Bb

23 BCb

24 BCb

17 ns

38

29

37

33

32 ns

38

37

37

38

RV: reference vegetation; CT: conventional tillage; NT: no-till; Sb: soybean; a Comparison between tillage systems CT-Sb, NT1-Sb, NT2-Sb, NT3-Sb and RV; Uppercase letters within the same column in each aggregate size class of each depth in each cropping system indicate the difference among RV and tillage treatments at P ≤ 0.05 by LSD. b

Comparison among tillage systems CT-Sb, NT1-Sb, NT2-Sb and NT3-Sb; Lowercase letters within the same column indicate difference between tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

150

Table 5.5


(ASI) in reference vegetation (RV) and different treatments in soybean-based cropping systems

Depth (cm)

0-5 5-10 10-20

Land use

MWD (mm)

MGD (mm)

ASI (%)

MWD (mm)

MGD (mm)

ASI (%)

MWD(mm)

MGD (mm)

ASI (%)

RVa

CT-Sbb

NT1-Sb

NT2-Sb

NT3-Sb

10.56 A

7.21 Cc

8.81 Bb

9.72 ABab

10.13 Aa

2.46 A

1.73 Dc

2.06 Cb

2.25 Ba

2.33 ABa

98.2 A

90.4 Cb

95.2 Ba

97.2 ABa

97.1 ABa

9.49 ns

7.75

8.01

7.68

8.30

2.27 ns

1.88

1.95

1.87

1.99

98.0 ns

94.4

95.8

94.3

95.3

7.17 ns

6.56

7.22

6.77

7.72

1.84 ns

1.70

1.82

1.74

1.92

95.3 ns

94.1

94.4

94.3

94.4

RV: reference vegetation; CT: conventional tillage; NT: no-till; Sb: soybean; a Comparison between tillage systems CT, NT1, NT2, NT3 and RV; Uppercase letters within the same column in each aggregate size class of each depth in each cropping system indicate the difference among RV and tillage treatments at P ≤ 0.05 by LSD. b Comparison among tillage systems CT, NT1, NT2 and NT3; Lowercase letters within the same column indicate difference between tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

151

Table 5.6


vegetation (RV) and different treatments in cassava-based cropping systems

Depth (cm)

Land use


8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5

5–10

10–20

RVa

CT-Csb

NT1-Cs

NT2-Cs

NT3-Cs

RV

CT-Cs

NT1-Cs

NT2-Cs

NT3-Cs

RV

CT-Cs

NT1-Cs

NT2-Cs

NT3-Cs

718 A

363 Cns

509 BC

579 AB

581 AB

619 ns

371

466

464

494

424 ns

291

376

392

361

99 ns

97

94

103

91

119 ns

98

104

108

102

129 ns

91

137

147

109

64 ns

97

82

80

66

98 ns

85

93

101

90

136 ns

113

130

126

117

34 C

134 Ans

94 AB

75 BC

78 BC

58 ns

112

105

114

93

113 ns

161

134

120

150

25 C

147 Ans

102 AB

75 BC

86 B

44 ns

157

118

110

106

97 ns

180

112

111

138

22 C

79 Ans

59 AB

40 BC

48 B

27 B

83 Ans

58 A

55 AB

58 A

53 ns

83

54

54

69

16 C

52 Ans

43 AB

30 BC

37 B

17 C

55 Aa

37 Bb

31 Bb

41 Bab

32 B

51 Aa

36 Bb

34 Bb

40 Bab

RV: reference vegetation; CT: conventional tillage; NT: no-till; Cs: cassava; a Comparison between tillage systems CT-Cs, NT1-Cs, NT2-Cs, NT3-Cs and RV; Uppercase letters within the same column in each aggregate size class of each depth in each cropping system indicate the difference among RV and tillage treatments at P ≤ 0.05 by LSD. b

Comparison among tillage systems CT-Cs, NT1-Cs, NT2-Cs and NT3-Cs; Lowercase letters within the same column indicate difference between tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

152

Table 5.7


(ASI) in reference vegetation (RV) and different treatments in cassava-based cropping systems

Depth (cm)

0-5 5-10 10-20

Land use

MWD (mm)

MGD (mm)

ASI (%)

MWD (mm)

MGD (mm)

ASI (%)

MWD (mm)

MGD (mm)

ASI (%)

RVa

CT-Csb

NT1-Cs

NT2-Cs

NT3-Cs

10.56 A

6.12 C ns

7.92 BC

8.87 AB

8.79 AB

2.46 A

1.60 Cns

1.89 BC

2.09 B

2.04 B

98.2 A

90.9 C ns

93.6 BC

96.1 AB

95.0 AB

949 ns

6.18

7.46

7.49

7.79

2.27 ns

1.59

1.84

1.87

1.87

98.0 A

90.4 Cns

94.2 B

95.2 AB

93.9 BC

7.17 ns

5.22

6.60

6.84

6.24

1.84 ns

1.48

1.74

1.78

1.66

95.3 A

89.0 Bns

94.4 A

94.9 A

92.9 AB

RV: reference vegetation; CT: conventional tillage; NT: no-till; Cs: cassava; a Comparison between tillage systems CT, NT1, NT2, NT3 and RV; Uppercase letters within the same column in each aggregate size class of each depth in each cropping system indicate the difference among RV and tillage treatments at P ≤ 0.05 by LSD. b Comparison among tillage systems CT, NT1, NT2 and NT3; Lowercase letters within the same column indicate difference between tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

153

Table 5.8

Concentrations of aggregate-associated SOC (g kg-1) in aggregate size classes under rice-based

cropping systems

Depth (cm)

Land use


8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5

5–10

10–20

RVa

CT-Rcb

NT1-Rc

NT2-Rc

NT3-Rc

RV

CT-Rc

NT1-Rc

NT2-Rc

NT3-Rc

RV

CT-Rc

NT1-Rc

NT2-Rc

NT3-Rc

26.2 A

17.7 Bns

18.4 B

19.4 B

19.9 B

19.2 A

16.0 BCns

15.8 C

16.9 ABC

18.2 AB

14.6 ns

14.6

15.4

14.5

15.7

23.5 ns

17.8

20.0

19.5

20.2

18.1 ns

16.1

15.9

17.1

18.7

15.0 ns

13.8

14.4

13.9

15.0

21.9 ns

17.1 b

18.2 ab

19.1 a

19.3 a

17.2 ns

15.1

15.5

16.2

17.5

13.9 ns

13.2

13.8

13.3

14.1

21.3 ns

16.7

17.4

18.2

18.2

16.5 ns

15.3

14.9

15.9

17.3

12.5 ns

12.9

12.9

13.3

13.9

21.5 ns

15.6

16.9

17.4

17.4

16.7 ns

14.8

14.9

15.5

16.4

12.4 ns

13.1

12.6

13.2

13.7

23.4 ns

15.6

16.7

17.7

17.4

17.7 ns

14.9

15.0

15.8

17.2

13.1 ns

12.7

12.4

12.8

14.0

29.3 A

16.5 Bns

18.6 B

19.1 B

19.3 B

21.6 A

15.8 Bns

15.8 B

16.5 B

17.5 B

14.7 ns

13.0

13.1

13.4

14.5

RV: reference vegetation; CT: conventional tillage; NT: no-till; Rc: rice; a Comparison between tillage systems CT-Rc, NT1-Rc, NT2-Rc, NT3-Rc and RV; Uppercase letters within the same column in each aggregate size class of each depth indicate the difference among RV and tillage treatments at P ≤ 0.05 by LSD. b Comparison among tillage systems CT-Rc, NT1-Rc, NT2-Rc and NT3-Rc; Lowercase letters within the same column indicate difference between tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

154

Table 5.9

Concentrations of aggregate-associated total N (g kg-1) in aggregate size classes under rice-


Depth (cm)

Land use


8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5

5–10

10–20

RVa

CT-Rcb

NT1-Rc

NT2-Rc

NT3-Rc

RV

CT-Rc

NT1-Rc

NT2-Rc

NT3-Rc

RV

CT-Rc

NT1-Rc

NT2-Rc

NT3-Rc

2.57 A

1.76 Bns

1.77 B

1.83 B

1.89 B

1.92 ns

1.62

1.65

1.71

1.73

1.54 ns

1.54

1.50

1.58

1.63

2.24 ns

1.86

1.71

1.89

1.86

1.76 ns

1.61

1.61

1.73

1.73

1.58 ns

1.45

1.53

1.54

1.55

2.10 ns

1.74

1.69

1.76

1.84

1.67 ns

1.53

1.51

1.67

1.70

1.43 ns

1.38

1.42

1.48

1.54

2.06 ns

1.65

1.72

1.75

1.79

1.75 ns

1.53

1.55

1.63

1.71

1.39 ns

1.45

1.39

1.44

1.51

2.09 ns

1.64

1.63

1.74

1.75

1.75 ns

1.51

1.53

1.55

1.61

1.41 ns

1.38

1.37

1.44

1.51

2.24 ns

1.66

1.61

1.70

1.74

1.77 A

1.45 Cb

1.53 BCab

1.68 ABa

1.64 ABa

1.44 ns

1.35

1.37

1.41

1.47

2.95 A

1.70 Bns

1.68 B

1.91 B

1.87 B

2.16 A

1.53 Bns

1.61 B

1.66 B

1.74 B

1.53 ns

1.39

1.43

1.53

1.55


155

Table 5.10

Concentrations of aggregate-associated POXC (g kg-1) in aggregate size classes under rice-


Depth (cm)

Land use


8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5

5–10

10–20

RVa

CT-Rcb

NT1-Rc

NT2-Rc

NT3-Rc

RV

CT-Rc

NT1-Rc

NT2-Rc

NT3-Rc

RV

CT-Rc

NT1-Rc

NT2-Rc

NT3-Rc

2.76 A

1.79 Cc

2.00 Cb

2.28 Ba

2.32 Ba

2.12 A

1.69 Bns

1.74 B

1.89 B

1.89 B

1.90 ns

1.51

1.62

1.74

1.73

2.52 A

1.76 Cc

1.94 BCb

2.19 ABa

2.22 ABa

2.05 ns

1.76

1.73

1.97

1.92

1.67 ns

1.43

1.59

1.66

1.73

2.34 A

1.77 Cb

1.85 BCb

2.17 ABa

2.15 ABCa

2.04 ns

1.70

1.78

1.88

1.87

1.68 ns

1.43

1.53

1.58

1.69

2.38 ns

1.71 c

1.87 b

2.13 a

2.10 a

1.99 ns

1.68

1.73

1.82

1.83

1.60 AB

1.38 Cc

1.50 BCbc

1.63 ABab

1.71 Aa

2.33 ns

1.68 b

1.75 b

2.07 a

2.13 a

1.97 ns

1.64

1.70

1.73

1.84

1.57 ns

1.42

1.49

1.59

1.68

2.44 A

1.72 Bb

1.76 Bb

2.08 ABa

2.05 ABa

2.08 ns

1.65

1.75

1.78

1.90

1.69 ns

1.43

1.46

1.64

1.67

3.39 A

1.75 Cb

1.92 BCab

2.18 Ba

2.14 BCa

2.28 A

1.68 Cb

1.79 BCab

1.87 BCa

1.95 Ba

1.70 ns

1.49

1.69

1.75

1.78


156

Table 5.11

C management index (CMI) of aggregate size classes under rice-based cropping systems

Depth (cm)

Land use


8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5

5–10

10–20

CT-Rc

NT1-Rc

NT2-Rc

NT3-Rc

CT-Rc

NT1-Rc

NT2-Rc

NT3-Rc

CT-Rc

NT1-Rc

NT2-Rc

NT3-Rc

65 c

73 b

84 a

86 a

79 ns

83

89

89

80 ns

86

94

94

70 b

77 b

89 a

89 a

86 ns

85

97

93

87 ns

98

102

109

76 b

80 b

95 a

93 a

83 ns

88

92

91

85 ns

91

94

104

74 c

81 b

94 a

91 a

83 ns

88

91

90

85 b

94 ab

102 a

109 a

74 b

76 b

93 a

95 a

84 ns

89

89

95

89 ns

95

101

108

73 b

74 b

88 a

87 a

80 ns

86

86

93

84 ns

86

98

100

51 b

56 ab

65 a

64 a

74 ns

80

83

86

89 ns

103

106

108

CT: conventional tillage; NT: no-till; Rc: rice; Lowercase letters within the same column in each aggregate size class of each depth indicate the difference among RV and tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

157

Table 5.12

Concentrations of aggregate-associated SOC (g kg-1) in aggregate size classes under soybean-


Depth (cm)

Land use


8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5

5–10

10–20

RVa

CT-Sbb

NT1-Sb

NT2-Sb

NT3-Sb

RV

CT-Sb

NT1-Sb

NT2-Sb

NT3-Sb

RV

CT-Sb

NT1-Sb

NT2-Sb

NT3-Sb

26.2 A

18.7 Bb

20.0 Bab

20.1 Bab

21.2 Ba

19.2 A

16.3 Cb

17.9 ABCa

17.2 BCab

18.1 ABa

14.6 B

16.0 A ns

16.6 A

16.2 A

16.2 A

23.5 ns

18.4 c

20.4 ab

19.1 bc

20.9 a

18.1 ns

16.1

17.9

17.1

18.6

15.0 ns

14.8

16.5

15.6

15.5

21.9 ns

18.1

19.5

18.8

20.0

17.2 ns

15.4 b

17.3 a

16.5 ab

17.9 a

13.9 ns

14.4

15.9

14.9

15.0

21.3 ns

17.4

18.4

18.0

18.7

16.5 ns

15.2

16.7

16.1

17.6

12.5 ns

14.5

15.6

15.1

15.0

21.5 ns

17.0

17.6

17.9

17.9

16.7 ns

15.1

16.5

16.0

17.3

12.4 ns

13.9

15.5

14.8

14.8

23.4 ns

16.9

17.4

16.9

17.5

17.7 ns

14.5

16.8

15.7

17.4

13.1 ns

13.6

15.6

14.9

14.7

29.3 A

17.4 B ns

18.5 B

17.5 B

19.7 B

21.6 ns

15.3

17.3

16.6

18.1

14.7 ns

14.1

16.1

15.5

15.0

RV: reference vegetation; CT: conventional tillage; NT: no-till; Sb: soybean; a Comparison between tillage systems CT-Sb, NT1-Sb, NT2-Sb, NT3-Sb and RV; Uppercase letters within the same column in each aggregate size class of each depth indicate the difference among RV and tillage treatments at P ≤ 0.05 by LSD. b Comparison among tillage systems CT-Sb, NT1-Sb, NT2-Sb and NT3-Sb; Lowercase letters within the same column indicate difference between tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

158

Table 5.13

Concentrations of aggregate-associated total N (g kg-1) in aggregate size classes under soybean-


Depth (cm)

Land use


8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5

5–10

10–20

RVa

CT-Sbb

NT1-Sb

NT2-Sb

NT3-Sb

RV

CT-Sb

NT1-Sb

NT2-Sb

NT3-Sb

RV

CT-Sb

NT1-Sb

NT2-Sb

NT3-Sb

2.57 A

1.65 B ns

1.80 B

1.82 B

1.90 B

1.92 A

1.37 Cc

1.52 BCbc

1.67 ABab

1.77 ABa

1.54 AB

1.36 Cb

1.38 BCb

1.61 Aa

1.60 Aa

2.24 ns

1.62

1.77

1.87

1.84

1.76 ns

1.38

1.53

1.68

1.76

1.58 ns

1.33 bc

1.29 c

1.58 ab

1.61 a

2.10 ns

1.63 b

1.68 b

1.86 a

1.77 ab

1.67 ns

1.31

1.53

1.67

1.65

1.43 ns

1.32

1.30

1.54

1.56

2.06 ns

1.50

1.70

1.75

1.71

1.75 ns

1.31

1.48

1.54

1.68

1.39 ns

1.26

1.30

1.49

1.50

2.09 ns

1.61

1.67

1.73

1.67

1.75 ns

1.30

1.47

1.56

1.65

1.41 ns

1.24 b

1.26 b

1.52 a

1.51 a

2.24 ns

1.40 b

1.61 a

1.67 a

1.73 a

1.77 A

1.30 C ns

1.42 BC

1.63 AB

1.69 AB

1.44 ns

1.32

1.21

1.52

1.55

2.95 A

1.47 Bb

1.66 Ba

1.71 Ba

1.66 Ba

2.16 A

1.41 B ns

1.45 B

1.63 B

1.75 AB

1.53 ns

1.30

1.24

1.51

1.54


159

Table 5.14

Concentrations of aggregate-associated POXC (g kg-1) in aggregate size classes under soybean-


Depth (cm)

Land use


8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5

5–10

10–20

RVa

CT-Sbb

NT1-Sb

NT2-Sb

NT3-Sb

RV

CT-Sb

NT1-Sb

NT2-Sb

NT3-Sb

RV

CT-Sb

NT1-Sb

NT2-Sb

NT3-Sb

2.76 A

1.86 Cb

2.27 Ba

2.33 Ba

2.38 Ba

2.12 ns

1.83

2.04

2.29

2.23

1.90 ns

1.73

1.88

1.90

1.92

2.52 ns

1.90 b

2.27 a

2.34 a

2.35 a

2.05 ns

1.86

1.99

2.15

2.17

1.67 ns

1.62

1.85

1.88

1.86

2.34 ns

1.89

2.22

2.26

2.28

2.04 ns

1.75

1.95

1.89

1.95

1.68 ns

1.55

1.85

1.82

1.85

2.38 ns

1.81

2.15

2.14

2.14

1.99 ns

1.75

1.91

1.82

1.87

1.60 ns

1.53

1.81

1.81

1.78

2.33 ns

1.76

1.98

2.07

2.12

1.97 ns

1.75

1.88

1.79

1.92

1.57 ns

1.47

1.77

1.79

1.99

2.44 ns

1.80

2.09

2.02

2.12

2.08 ns

1.79

1.85

1.88

1.88

1.69 ns

1.62

1.95

1.94

1.89

3.39 A

1.84 B ns

2.15 B

2.22 B

2.30 B

2.28 ns

1.88

1.95

1.88

1.91

1.70 ns

1.63

1.77

1.85

1.94


160

Table 5.15

C management index (CMI) of aggregate size classes under soybean-based cropping systems

Depth (cm)

Land use


8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5

5–10

10–20

CT-Sb

NT1-Sb

NT2-Sb

NT3-Sb

CT-Sb

NT1-Sb

NT2-Sb

NT3-Sb

CT-Sb

NT1-Sb

NT2-Sb

NT3-Sb

68 b

83 a

86 a

88 a

87 ns

97

112

107

93 ns

100

103

104

76 b

91 a

96 a

95 a

92 ns

97

108

107

103 ns

113

118

117

81

97

100

100

86 ns

95

92

95

94 ns

110

111

113

76 ns

94

94

93

89 ns

95

91

94

96 ns

112

113

112

76 ns

87

93

95

92 ns

97

92

100

94 ns

111

114

128

75 ns

90

88

91

90 ns

89

92

91

98 ns

117

118

115

54 ns

64

68

69

86 ns

87

83

85

98 ns

103

111

119

CT: conventional tillage; NT: no-till; Sb: soybean; Lowercase letters within the same column in each aggregate size class of each depth indicate the difference among RV and tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

161

Table 5.16

Concentrations of aggregate-associated SOC (g kg-1) in aggregate size classes under cassava-


Depth (cm)

Land use


8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5

5–10

10–20

RVa

CT-Csb

NT1-Cs

NT2-Cs

NT3-Cs

RV

CT-Cs

NT1-Cs

NT2-Cs

NT3-Cs

RV

CT-Cs

NT1-Cs

NT2-Cs

NT3-Cs

26.2 A

16.3 B ns

17.2 B

17.6 B

18.1 B

19.2 A

16.3 B ns

16.1 B

16.9 B

17.8 AB

14.6 ns

14.5

14.1

14.8

15.6

23.5 A

16.4 B ns

16.9 B

17.4 B

17.8 B

18.1 ns

15.9

16.0

16.8

17.4

15.0 ns

13.4

13.9

14.8

15.4

21.9 ns

15.9

16.3

16.7

17.1

17.2 ns

15.4

15.4

15.8

16.6

15.0 ns

13.4

13.9

14.8

15.4

21.3 ns

15.1

15.6

15.6

16.6

16.5 ns

14.9

14.7

16.1

15.8

13.9 ns

12.9

13.5

14.5

15.0

21.5 ns

14.8

15.2

15.4

16.2

16.7 ns

14.8

14.7

15.5

15.5

12.5 ns

12.7

13.2

14.4

14.6

23.4 A

15.0 B ns

15.3 B

15.5 B

16.4 B

17.7 ns

15.0

15.2

15.6

15.6

13.1 ns

12.3

13.2

14.5

14.8

29.3 A

15.7 B ns

15.6 B

16.2 B

17.7 B

21.6 A

15.6 B ns

15.9 B

16.2 B

16.1 B

14.7 ns

12.6

13.7

14.9

15.1

RV: reference vegetation; CT: conventional tillage; NT: no-till; Cs: cassava; a Comparison between tillage systems CT-Cs, NT1-Cs, NT2-Cs, NT3-Cs and RV; Uppercase letters within the same column in each aggregate size class of each depth indicate the difference among RV and tillage treatments at P ≤ 0.05 by LSD. b Comparison among tillage systems CT-Cs, NT1-Cs, NT2-Cs and NT3-Cs; Lowercase letters within the same column indicate difference between tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

162

Table 5.17

Concentrations of aggregate-associated total N (g kg-1) in aggregate size classes under cassava-


Depth (cm)

Land use


8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5

5–10

10–20

RVa

CT-Csb

NT1-Cs

NT2-Cs

NT3-Cs

RV

CT-Cs

NT1-Cs

NT2-Cs

NT3-Cs

RV

CT-Cs

NT1-Cs

NT2-Cs

NT3-Cs

2.57 A

1.54 B ns

1.63 B

1.79 B

1.82 B

1.92 ns

1.54

1.64

1.69

1.77

1.54 ns

1.43

1.49

1.54

1.67

2.24 A

1.52 B ns

1.62 B

1.84 AB

1.81 AB

1.76 ns

1.57

1.62

1.70

1.60

1.58 ns

1.47

1.46

1.45

1.60

2.10 ns

1.46

1.57

1.71

1.72

1.67 ns

1.54

1.56

1.58

1.63

1.43 ns

1.37

1.40

1.41

1.53

2.06 ns

1.41

1.48

1.60

1.71

1.75 ns

1.39

1.50

1.47

1.51

1.39 ns

1.38

1.46

1.41

1.50

2.06 ns

1.40

1.53

1.60

1.77

1.75 ns

1.45

1.49

1.47

1.60

1.41 ns

1.41

1.40

1.42

1.51

2.24 ns

1.35

1.44

1.64

1.67

1.77 ns

1.41

1.58

1.44

1.49

1.44 ns

1.38

1.40

1.50

1.47

2.95 A

1.47 B ns

1.61 B

1.75 B

1.73 B

2.16 A

1.48 B ns

1.65 B

1.59 B

1.58 B

1.53 ns

1.41

1.49

1.48

1.62


163

Table 5.18

Concentrations of aggregate-associated POXC (g kg-1) in aggregate size classes under cassava-


Depth (cm)

Land use


8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5

5–10

10–20

RVa

CT-Csb

NT1-Cs

NT2-Cs

NT3-Cs

RV

CT-Cs

NT1-Cs

NT2-Cs

NT3-Cs

RV

CT-Cs

NT1-Cs

NT2-Cs

NT3-Cs

2.76 A

1.85 Cb

2.00 BCb

2.26 Ba

2.24 Ba

2.12 ns

1.70

1.86

2.03

2.08

1.90 ns

1.58

1.69

1.87

1.81

2.52 A

1.77 Cc

1.94 DCb

2.19 BCa

2.27 ABa

2.05 A

1.69 B ns

1.95 A

1.93 A

1.94 A

1.67 ns

1.41

1.74

1.81

1.75

2.34 ns

1.85

1.95

2.14

2.20

2.04 A

1.66 B ns

1.84 AB

1.81 B

1.86 AB

1.68 ns

1.38

1.64

1.85

1.71

2.38 ns

1.84 b

1.86 b

2.14 a

2.15 a

1.99 A

1.56 B ns

1.80 A

1.82 A

1.78 A

1.60 ns

1.35

1.73

1.85

1.63

2.33 ns

1.76 b

1.80 b

2.08 a

2.05 a

1.97 ns

1.51

1.80

1.76

1.77

1.57 ns

1.32

1.73

1.77

1.69

2.44 A

1.77 Bb

1.84 Bb

2.16 ABa

2.12 ABa

2.08 A

1.56 B ns

1.82 AB

1.85 AB

1.86 A

1.69 ns

1.29

1.71

1.87

1.81

3.39 A

1.79 Cb

1.91 BCb

2.25 Ba

2.12 BCa

2.28 A

1.62 B ns

1.89 B

1.89 B

1.84 B

1.70 ns

1.32

1.62

1.81

1.74


164

Table 5.19

C management index (CMI) of aggregate size classes under cassava-based cropping systems

Depth (cm)

Land use


8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5

5–10

10–20

CT-Cs

NT1-Cs

NT2-Cs

NT3-Cs

CT-Cs

NT1-Cs

NT2-Cs

NT3-Cs

CT-Cs

NT1-Cs

NT2-Cs

NT3-Cs

68 c

74 bc

85 a

83 ab

80 ns

89

97

100

86 ns

91

100

98

71 b

78 b

90 a

93 a

82 ns

97

94

95

86 ns

108

112

109

82 ns

86

94

98

81 ns

91

88

91

82 ns

98

111

103

81 b

82 b

95 a

96 a

77 ns

91

91

89

83 ns

110

116

102

78 c

80 bc

94 a

93 ab

76 ns

94

91

91

83 ns

111

113

107

75 c

78 bc

94 a

92 ab

74 ns

89

89

90

76 ns

102

111

101

53 b

57 b

68 a

64 a

72 ns

85

84

82

78 ns

96

108

104

CT: conventional tillage; NT: no-till; Cs: cassava; Lowercase letters within the same column in each aggregate size class of each depth indicate the difference among RV and tillage treatments at P ≤ 0.05 by LSD. ns: not significant.

165

Table 5.20

Pearson correlation coefficients between aggregate-associated SOC over size classes and soil

aggregation indices under rice-based cropping systems

Aggregate indices


8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5 cm

MWD

MGD

ASI

5–10 cm

MWD

MGD

ASI

10–20 cm

MWD

MGD

ASI

0.63*

0.66*

0.66*

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

0.62*

0.65*

0.63*

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

n = 12 per aggregate size class for all soil aggregation indices; MWD: mean weight diameter (mm); MGD: mean geometric diameter (mm); ASI: aggregate stability index (%); * P ≤ 0.05; ** P ≤ 0.01; ns: not significant at P ≤ 0.05.

166

Table 5.21


aggregation indices under soybean-based cropping systems

Aggregate indices


8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5 cm

MWD

MGD

ASI

5–10 cm

MWD

MGD

ASI

10–20 cm

MWD

MGD

ASI

0.62*

0.63*

0.59*

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

0.57*

0.58*

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

0.64*

0.63*

ns

ns

ns

ns

n = 12 per aggregate size class for all soil aggregation indices; MWD: mean weight diameter (mm); MGD: mean geometric diameter (mm); ASI: aggregate stability index (%); * P ≤ 0.05; ns: not significant at P ≤ 0.05.

167

Table 5.22


aggregation indices under cassava-based cropping systems

Aggregate indices


8–19 4–8 2–4 1–2 0.5–1 0.25–0.5 0.053–0.25

0–5 cm

MWD

MGD

ASI

5–10 cm

MWD

MGD

ASI

10–20 cm

MWD

MGD

ASI

0.75**

0.72**

0.77**

ns

ns

ns

ns

ns

ns

0.72**

0.70*

0.74**

ns

ns

ns

ns

ns

ns

0.64*

0.61*

0.68*

ns

ns

ns

ns

ns

ns

0.59*

ns

0.60*

ns

ns

ns

ns

ns

ns

0.66*

0.62*

0.66*

ns

ns

ns

ns

ns

ns

0.65*

0.61*

0.63*

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

0.61*

n = 12 per aggregate size class for all soil aggregation indices; MWD: mean weight diameter (mm); MGD: mean geometric diameter (mm); ASI: aggregate stability index (%); * P ≤ 0.05; ** P ≤ 0.01; ns: not significant at P ≤ 0.05.

168

CHAPTER 6

General Conclusions

The association or rotation of cover crops with main crops under CA systems that

produce high biomass inputs to the soil significantly increased total SOC in SbCS and CsCS in

the surface soil layer and the recovery trend of SOC in RcCS under CA might become evident

with time, particularly bi-annual crop rotations. The higher soil total N under CA in the three

cropping systems also observed. Although SOC was higher in the few surface layers but a

decrease in the deeper layers compared with CT was consistent. This might be related to the

continuous supply of fresh C through root biomass and exudates from cover crops during six-

month dry season, accelerating microbial activities that could decompose the native SOC with

their enzymes using the source of energy from fresh C. It is likely that the studied heavy clayed

Oxisols did not exhibit increased SOC in CT after five years due to soil texture, mineralogy and

the biomass-C inputs returned to soils after the harvest of main (i.e., rice, soybean, cassava,

maize) and preceding (i.e., mungbean, sesame) crops. However, a slight decrease in HWEOC

and POXC was noticed, which might potentially affect the loss of total SOC in a longer period.

In general, CA increased the storage of labile SOC fractions (i.e., POC, HWEOC, POXC)

and promoted soil enzymatic activities (i.e., β-glucosidase, arylsulfatase) especially at the 0-5 cm

soil layer. These results emphasize the positive impact of short-term CA through the absence of

soil disturbance and the importance of crops and their residues in accumulation of more SOC and

its labile fractions and changes in the biological functioning of the soil, with higher soil enzyme

activities in the topsoil. Thus, the labile SOC fractions and soil enzymes could serve as sensitive

indicators of SOC dynamics in short-term CA practices. In contrast, MAOC, PEOC and CSOC

were nearly constant in each depth among treatments in the three cropping systems, indicating

169

less impacted or higher stability of these SOC fractions following short-term changes in tillage

and crop rotation management. They represented the large portions of total SOC stocks.

The increased labile SOC fractions and soil enzyme activities under CA systems might

partially contribute to an increase in soil aggregate stability and in turn SOC was physically

protected and consequently to restore total SOC in the surface layers. Similar to RV, CA in the

three cropping systems increased the proportions of large macroaggregates (8-19 mm) leading to

an improvement of soil aggregation indices which positively correlated with the large

macroaggregate-associated SOC. Even no significant effects in RcCS and SbCS, increased large

macroaggregates and aggregate stability under CA play a crucial role in storage and stabilization

of SOC, total N and POXC within macroaggregate-occluded microaggregates. The aggregate-

associated POXC was more sensitive than SOC to the short-term CA systems resulting in greater

concentrations in the majority of aggregate size classes than CT in the topsoil. It was supported

by the results of CP-MAS 13C NRM measurement that indicated the continuous biomass-C

inputs under CA tended to increase the proportions of aliphatic C than under CT.

When comparing among the three CA systems, bi-annual crop rotations might be

recommended as an appropriate crop rotation scheme that provided greater potential to restore

total SOC, its labile fractions, soil enzymes, large macroaggregates and the concentrations of

SOC, total N and POXC associated with large macroaggregates in the studied topsoil in a short-

term period. Although deep rooting cover crops were included in the CA systems, it did not lead

to a significant change in subsoil layers. However, the results support the concept of high

potential to vertically distribute SOC to deeper layers over time resulting from the continuity of

their high biomass-C inputs. The recovery trends of the majority of measured parameters were

quite obvious in few subsurface soil layers as some significant differences were already apparent.

170

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